US20110288796A1 - Condition based monitoring system based on radar sensor - Google Patents

Condition based monitoring system based on radar sensor Download PDF

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US20110288796A1
US20110288796A1 US13/096,253 US201113096253A US2011288796A1 US 20110288796 A1 US20110288796 A1 US 20110288796A1 US 201113096253 A US201113096253 A US 201113096253A US 2011288796 A1 US2011288796 A1 US 2011288796A1
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radar
vibration
machine
sensors
sensor
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US9482573B2 (en
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Andy Peczalski
Dinkar Mylaraswamy
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Honeywell International Inc
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Honeywell International Inc
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Priority to EP11166288.8A priority patent/EP2390636B1/en
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H9/00Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means

Definitions

  • CBM Condition Based Maintenance
  • Vibration sensing using accelerometers is the standard measurement for machine monitoring. These sensors measure vibration at the location where they are attached to the machine.
  • accelerometers come in various forms, their basic principles remains the same—make physical contact with the machine being monitored and generate a signal that is proportional to the harmonic motion experienced at the point of contact. Permanently installed accelerometers are often attached to the machine with screws and wired connections. Besides their intrusive nature (designed while the machine is assembled), such sensors cannot be mounted on many moving parts, making it impossible to monitor “locations” that may be critical from a vibration standpoint.
  • Permanently mounted accelerometers are often complemented with handheld vibration monitoring equipment. However, signals generated from sensors mounted “far away” from a machine pick up background noises such as those generated by a helicopter body. This can obscure important signatures of failing gears or bearings. Further, it may not be safe to approach the machine with an attachable handheld sensor and try to make the sensor head reach the remote location of interest.
  • a sensing system comprises a radar-based vibration sensor and processing unit.
  • the radar-based vibration sensor is configured to obtain vibration data from mechanical operation of a component or series of components in a machine of interest.
  • the processing unit is configured to analyze the data obtained by the at least one radar-based vibration sensor, and provide indications related to a status of the mechanical operation of the one or more components in the machine of interest.
  • data from multiple vibration sensors may be fused and factored in the processing system, such as data collected from a plurality of radar-based vibration sensors, and a plurality of machine-mounted vibration sensors (i.e., accelerometers).
  • a steering system may be configured to direct and steer the radar-based vibration sensor to collect the vibration data from different locations of mechanical operation for the machine of interest. From the indications calculated from the processing unit, various machine health indicators and potential maintenance actions may be suggested.
  • FIG. 1 depicts an example operation of a sensor based system configured for monitoring a selected machine
  • FIG. 2 depicts an example operation of a sensor based system configured for monitoring a plurality of machines
  • FIG. 3 depicts an example use of a sensor based system to collect data on a wind turbine gearbox
  • FIG. 4 depicts an example use of a sensor based system to perform real-time data analysis using a handheld vibration monitoring device
  • FIG. 5 depicts a block diagram of a computer system enabled to perform data analysis from various sensors.
  • the functions or algorithms described herein may be implemented in software or a combination of software and human or enterprise implemented procedures in one embodiment.
  • the software may consist of computer executable instructions stored on computer readable media such as memory or other type of storage devices. Further, such functions correspond to modules, which are software, hardware, firmware, or any combination thereof. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples.
  • the software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system.
  • one or more radar-based displacement sensors may be used to gather vibration information from an operating machine.
  • the radar-based displacement sensor may be steered or directed toward multiple regions of interest on the operating machine to gather vibration information from the multiple regions of interest.
  • the data produced from the one or more radar based displacement sensors may be processed in a condition monitoring system or other sensing system that allows monitoring of the entire machine and a plurality of sensing locations.
  • a condition monitoring system or other sensing system that allows monitoring of the entire machine and a plurality of sensing locations.
  • maintenance information and other monitoring data may be extracted from the sensing system and provided to a user for remedial action.
  • accelerometers Despite an accelerometer's ability to monitor instrumented parts of the machine, relying on the use of accelerometers in a condition monitoring system or like sensing system may result in a number of location and design limitations (particularly for permanent sensor installation). Further, economic reasons may limit the locations where accelerometers can be installed and deployed.
  • Tethered accelerometer heads suffer the same limitations as its permanently installed counterpart. That is, tethered sensors need to make mechanical contact with the machine and often require supplemental measurements such as an optical tachometer. Handheld accelerometers can provide a wider range, but their accuracy depends on the skill level of the technician. Further, it may not be safe to approach the machine with an attachable handheld sensor and try to make the sensor head reach the remote location of interest.
  • a sensing system such as the example system illustrated in FIG. 1 may be used to monitor an entire machine with moving parts, using a scanning radar-based displacement sensor, permanently mounted sensors, and back-end software to enable maintenance related decision making.
  • Structural problems can develop anywhere in the machine and are not restricted to the location where embedded sensors may be located. Collecting data with the use of scanning radar-based vibration sensors provides the ability to monitor large areas of the “entire” machine, while also analyzing well-known weak spots in the machine as necessary for rapid, accurate assessment of machine health.
  • the presently disclosed sensing system may be deployed to monitor a wide variety of rotating machinery and mechanical components within a machine.
  • This rotating machinery may include one or more of an electric motor, pulleys, gearboxes, shafts, bearings, drivelines, and like mechanical parts.
  • abnormal harmonic readings may indicate rotational misbalance as a result from a failing or misbalanced component.
  • the sensing system may process these harmonic readings and other temporal and spectral data to detect the status of various operational components.
  • the sensing system may specifically generate health indicators to alert users to warnings and failures such as stator breaks, rotor bending, wiring, shaft misalignment, gear tooth breaks, and bearing spalls.
  • Use of the displacement vibration sensor within the sensing system provides a non-intrusive, non-contact approach to detect failures without adding conventional accelerometers. This not only makes it easy to retrofit legacy machines, but also allows the same apparatus to monitor more than one machine at the same time.
  • An example sensing system includes several components used to provide sensing measurements and direct operation of sensors as necessary.
  • the system may include a stand-off sensor that observes the machine for vibration induced displacements, one or more of permanently mounted sensors that provide measurement of machine condition at point locations, a steering system that steers the stand-off sensor to observe specific regions of interest within the machine, a software module that analyzes the data from various sensors, and a decision support module that calculates the machine health indicators and potential maintenance actions.
  • the stand-off sensor is a radar based displacement sensor.
  • the stand-off sensor may use Doppler-based radar techniques to transmit and measure signals aimed at a specific object of interest.
  • the stand-off sensor may be electronically or mechanically steered towards the specific object for monitoring.
  • the steering may be either periodic or trigger-based, and driven by any combination of human and automated control.
  • a steering subsystem may be used to position an electromechanically operated phased-array radar antenna within the stand-off sensor.
  • a non-contact, radar-based sensor may be configured to sense vibration from a considerable distance (e.g., 4 feet), and provide a wide field of view that could be adjusted to monitor the entire machine or specific parts of the machine. Further, the sensor may be tuned to detect only the motion of the machinery and reject background vibration, providing vibration data from under-instrumented parts of the machine without incurring additional costs. This makes acquiring vibration data from previously un-instrumented parts not only cost-effective, but also safe.
  • the radar antenna of the sensor may also be configured to have a tunable or narrow field of view, such as providing no more than a 10-degree field of view and therefore could be deployed as a handheld or spot sensor.
  • a radar-based displacement sensor used in the presently disclosed sensing system transmits RF energy toward a target area to be monitored.
  • the RF energy reflects from metal surfaces and edges within the target area and returns to the sensor.
  • the sensor may be calibrated to reject any movement that is common to the target and the antenna.
  • the sensing system may process and analyze data from a plurality of displacement sensors or in combination with a plurality of fixed accelerometers to identify and reject invalid data.
  • the data produced by the radar-based displacement sensor may be therefore refined or processed at the sensor or within a processing module separate to the sensor.
  • Machine 110 may contain a plurality of moving parts or components that are intended to be monitored to ensure proper mechanical operation.
  • a series of accelerometers 121 , 122 , 123 , 124 may be attached to or otherwise directly proximate to various moving components of the machine 110 .
  • a radar sensor 130 may be positioned auxiliary to accelerometers 121 , 122 , 123 , 124 , or may be positioned as a portable stand alone sensor. For example, an operator may bring the radar sensor 130 to a factory floor and position it on a tripod at a distance (such as less than four feet) from the machine 110 when suspecting performance issues, or when the machine operation is in need of a period checkup.
  • the sensor 130 may be configured to collect vibration or displacement data for a period of time, such as over a few days, allowing the data collected over this period of time to be analyzed for indicators of possible problems such as motor damage or wear.
  • the radar sensor 130 transmits and receives radar energy 131 to and from the machine 110 , collecting vibration data from the whole or a large part of the machine.
  • the vibration data including temporal or spectral data collected from the combination of accelerometers 121 , 122 , 123 , 124 and the radar sensor 130 is then transmitted to an analysis component 150 as represented by lines 141 , 142 , 144 , 145 , and 143 respectively.
  • the lines may represent hardwired communication lines, or wireless connections in various embodiments.
  • a certain measured threshold of change e.g., an amplitude of velocity or frequency shift/new frequency
  • a certain measured threshold of change may be configured to create or otherwise initiate a request for maintenance within the analysis component 150 .
  • information from the radar sensor 130 may be collected as a time sampled data or Fast Fourier Transform (FFT) of the data or frequencies and amplitudes of the spectrum peaks.
  • FFT Fast Fourier Transform
  • Information from different peaks can be combined, for example, to enable the velocity at the moving parts of the machine to be correlated with the load.
  • the loading condition may be used for the interpretation of the spectral peaks corresponding to vibrations which may shift or broaden for different loads, such as may be measured from bearing vibrations.
  • FIG. 2 illustrates another embodiment providing a configuration of a sensor system 200 configured to monitor operation of multiple machines or components with use of a radar-based sensor.
  • machine A 211 and machine B 212 are monitored with a series of permanently mounted sensors, such as accelerometers 221 , 222 , and 223 , 224 respectively.
  • the radar-based sensor 230 may configured to direct RF energy towards one or both of machine A 211 and machine B 212 as shown in 231 and 232 .
  • a steering motion may be applied to the radar-based sensor 230 to aim the sensor and direct a single source of RF energy towards one of the machines at a single point in time. This steering motion may be communicated to the radar sensor through use of motion instructions 233 .
  • the radar sensor 230 provides multiple sources of energy to achieve simultaneous monitoring of multiple machines at a single point in time.
  • the temporal or spectral data from the sensor 230 is then transmitted as represented at 243 to a data analysis component 240 .
  • data from the mounted accelerometers 221 , 222 , 223 , 224 is communicated as represented by lines 241 , 242 , 244 , and 245 .
  • the data analysis component 250 may then fuse in the data from the variety of the permanently mounted sensors 221 , 222 , 223 , 224 and the radar-based sensor 230 to determine the status of larger machine operations as a whole, or the status of specific machine subsystems and processes, based on the comparison of data from a plurality of sensors. This may result in the generation of user indications, such as one or more recommended maintenance actions 260 , the generation of health indicators 270 , or other useful machine-related information.
  • the radar-based sensor may be configured to detect machine vibration and misbalance (such that occurring from bearing damage) at a distance without making any contact with the machine.
  • the data produced from these remote measurements may even exceed the performance of a high-quality screw accelerometer mounted directly on the bearing enclosure.
  • FIG. 3 provides an illustration of an example monitoring system using a combination of radar-based and permanently mounted sensors to monitor a mechanical system, specifically a horizontal axis wind turbine 300 . As depicted, a number of sensors are depicted as being used to monitor the operation of the electrical generating components of the turbine 300 within its nacelle 302 .
  • two radar-based vibration sensors 310 , 312 are placed within the nacelle 302 and adjacent to the mechanical drive used to ultimately rotate generator 320 .
  • the first radar-based vibration sensor 310 is configured to monitor the high-speed shaft 322 located between generator 320 and gear box 324 .
  • the second radar-based vibration sensor 312 is configured to monitor the main shaft 330 located between transmission 326 and hub 334 , specifically the portion of the main shaft extending between bearings 328 and 332 .
  • the data from multiple sensors such as radar-based vibration sensors 310 , 312 may be compared with each other in the sensing system to compare relative vibration or other harmonics that are constant in operation of the system.
  • the data collected from the radar-based vibration sensors 310 , 312 may then be transmitted from the sensors to an off-set processing system, using any of a number of wired or wireless networking data connections.
  • FIG. 4 provides another illustration of use of the sensor-based system with a portable vibrometer embodiment, specifically in conjunction with a handheld inspection device.
  • operator 400 aims a radar-based sensor 410 towards a mechanical system; illustrated here as a high-pressure pump 420 such as may be used in a large pump farm.
  • the sensor 410 transmits and receives the RF radio signal 430 from one or more moving parts within the high pressure pump 420 .
  • the operator uses some type of a readout display unit to assist with focusing the radar sensor and to obtain feedback.
  • the radio frequency data obtained at the radar sensor 410 may be transmitted to a computing device, here a portable handheld measurement device 440 having a display screen interface 450 .
  • the handheld measurement device 440 is able to process the RF signals obtained from sensor 410 and provide immediate feedback and measurements related to the sensor readings.
  • the data may be transmitted from the radar sensor to another computing system, such as being transmitted from the handheld measurement device 440 using a network connection to an off-site computing system (not illustrated) for analysis and processing.
  • the handheld measurement device 440 may be configured to provide a visual indication 450 of the status of the sensor measurements to the operator 400 .
  • This visual indication may be presented as a graph rendered on a display screen, a textual indicator of measurements, or another illustration or indication rendered on the display screen.
  • a variety of other graphical, textual, audio, or tactile indications may be provided to the user through the device 440 to indicate warnings, failures, and other malfunctions of the monitored equipment.
  • the radar-based displacement sensor generates RF frequency at 24 GHz, an unregulated frequency band.
  • the reflection phase changes proportionally to the displacement of the reflective surface relative to the radar divided by the RF frequency signal wavelength.
  • the wavelength of the signal is short (e.g., 1.25 cm) for high sensitivity of the sensor.
  • the reflected signal is modulated by the target vibration magnitude, such that any movement that is common to the target and the antenna is rejected.
  • the return signals are mixed (beat against each other) with transmitted signals.
  • the output signal phase of the sensor follows the radial displacement (in a direction perpendicular to the antenna) of the target in the time domain.
  • the output signal is converted in the frequency domain by Fast Fourier Transform (FFT). If the reflecting surfaces in the radar antenna's field of view move at different frequencies or amplitudes, they will contribute different spectral peaks in the sensor signal. Thus, one sensor with a wide field of view is capable of monitoring many moving parts at the same time.
  • FFT Fast Fourier Transform
  • the sensor may provide high sensitivity that decreases for longer distances, and may also provide an output signal that is decreasing with increased distance.
  • a sensor may be configured to detect displacement as small as 0.1 nm at a distance of 50 cm and 0.5 nm at a distance of 133.5 cm.
  • the very high sensitivity is due to the very short round-trip time for the return signal (e.g., 3 nsec for 50 cm). Therefore, the local oscillator does not drift much and the phase noise of the sensor is very low. The round-trip time and thus the phase noise are larger for longer distances.
  • the amplitude of the sensor decreases proportionally to the distance because the other half of the mixing energy comes from the local oscillator in the sensor and does not change with the distance. Therefore, large sensing distances are feasible for comparatively low transmission power (e.g., 50 mW).
  • the radar-based sensor may be used to detect higher harmonic side-bands and higher harmonics than that detected by an accelerometer. Moreover the signal-to-noise ratio (energy in the sidebands divided by the noise energy around the sidebands) may be higher for a radar-based sensor than in an accelerometer.
  • a radar-based sensor can be tuned to wide angles or otherwise tuned to a specific field of view to monitor the entire machine or specific parts of the machine, while detecting the motion of the machinery and rejecting background vibration. Therefore, use of a radar-based sensor may have a higher probability of detecting misbalance faults than an accelerometer.
  • the cost for the sensing components may be an order of magnitude lower.
  • Some embodiments may be capable of wide field sensing with no moving parts. Further embodiments may penetrate nonconductive protection layers, and various embodiments may be made without the need for delicate optical components and connections.
  • the radar-based sensor and sensing system may be configured to reject the common mode vibration that may be obstructing the fault signature in machines as well as its insensitivity to surface fouling. For example, this limits optical sensors in weakly supported platforms such as a helicopter body or the gearbox in a wind turbine nacelle.
  • warnings and other usable information can be generated and transmitted in a rapid fashion with significant advantages over existing sensing techniques.
  • FIG. 5 A block diagram of an example computer system that performs data analysis from the various sensors, and includes a decision support module that calculates the machine health indicators and potential maintenance actions and executes other necessary analysis and programming is shown in FIG. 5 .
  • a general computing device in the form of a computer 510 may include a processing unit 502 , memory 504 , removable storage 512 , and non-removable storage 514 .
  • Memory 504 may include volatile memory 506 and non-volatile memory 508 .
  • Computer 510 may include—or have access to a computing environment that includes—a variety of computer-readable media, such as volatile memory 506 and non-volatile memory 508 , removable storage 512 and non-removable storage 514 .
  • Computer storage includes random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM) & electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium capable of storing computer-readable instructions.
  • Computer 510 may include or have access to a computing environment that includes input 516 , output 518 , and a communication connection 520 .
  • the computer may operate in a networked environment using a communication connection to connect to one or more remote computers.
  • the remote computer may include a personal computer (PC), server, router, network PC, mobile device, a peer device or other common network node, or the like.
  • the communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN) or other networks.
  • LAN Local Area Network
  • WAN Wide
  • Computer-readable instructions to execute methods and algorithms described above may be stored on a computer-readable medium such as illustrated at a program storage device 525 are executable by the processing unit 502 of the computer 510 .
  • a hard drive, CD-ROM, and RAM are some examples of articles including a computer-readable medium.
  • a user interface is provided in connection with the computer system, such as a touch screen device for providing both input 516 and output 518 .
  • the presently described sensing system and radar-based sensors may be implemented in a variety of settings, including but not limited to industrial and aerospace applications. Those skilled in the art would recognize that variations to the presently described embodiments may be used to apply the presently disclosed techniques to a variety of other mechanical and electromechanical applications and fields of use.

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  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

A sensing system comprising a radar-based vibration sensor and processing unit used to collect and process vibration information from a machine of interest. The radar-based vibration sensor obtains vibration data from mechanical operation of a component or series of components in the machine, and may be steered toward specific regions of interest of the machine. The processing unit analyzes the data, and may fuse data from a plurality of vibration sensors, such as radar-based vibration sensors and multiple machine-mounted sensors such as accelerometers. From this analysis, indications related to a status of the mechanical operation of the components in the machine of interest may be provided to relevant users.

Description

    CLAIM OF PRIORITY
  • This patent application claims the benefit of priority, under 35 U.S.C. Section 119(e), to U.S. Provisional Patent Application Ser. No. 61/347,762, entitled “CONDITION BASED MONITORING SYSTEM BASED ON RADAR SENSOR”, filed May 24, 2010, which is hereby incorporated by reference in its entirety.
  • BACKGROUND
  • Machines with moving parts need predictive maintenance to lower production costs. As a part of Condition Based Maintenance (CBM), the timing and need for maintenance can be predicted with a condition monitoring system. Vibration sensing using accelerometers is the standard measurement for machine monitoring. These sensors measure vibration at the location where they are attached to the machine.
  • While accelerometers come in various forms, their basic principles remains the same—make physical contact with the machine being monitored and generate a signal that is proportional to the harmonic motion experienced at the point of contact. Permanently installed accelerometers are often attached to the machine with screws and wired connections. Besides their intrusive nature (designed while the machine is assembled), such sensors cannot be mounted on many moving parts, making it impossible to monitor “locations” that may be critical from a vibration standpoint.
  • Permanently mounted accelerometers are often complemented with handheld vibration monitoring equipment. However, signals generated from sensors mounted “far away” from a machine pick up background noises such as those generated by a helicopter body. This can obscure important signatures of failing gears or bearings. Further, it may not be safe to approach the machine with an attachable handheld sensor and try to make the sensor head reach the remote location of interest.
  • SUMMARY
  • In one specific embodiment, a sensing system comprises a radar-based vibration sensor and processing unit. The radar-based vibration sensor is configured to obtain vibration data from mechanical operation of a component or series of components in a machine of interest. The processing unit is configured to analyze the data obtained by the at least one radar-based vibration sensor, and provide indications related to a status of the mechanical operation of the one or more components in the machine of interest.
  • In further embodiments, data from multiple vibration sensors may be fused and factored in the processing system, such as data collected from a plurality of radar-based vibration sensors, and a plurality of machine-mounted vibration sensors (i.e., accelerometers). In other further embodiments, a steering system may be configured to direct and steer the radar-based vibration sensor to collect the vibration data from different locations of mechanical operation for the machine of interest. From the indications calculated from the processing unit, various machine health indicators and potential maintenance actions may be suggested.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • FIG. 1 depicts an example operation of a sensor based system configured for monitoring a selected machine;
  • FIG. 2 depicts an example operation of a sensor based system configured for monitoring a plurality of machines;
  • FIG. 3 depicts an example use of a sensor based system to collect data on a wind turbine gearbox;
  • FIG. 4 depicts an example use of a sensor based system to perform real-time data analysis using a handheld vibration monitoring device; and
  • FIG. 5 depicts a block diagram of a computer system enabled to perform data analysis from various sensors.
  • DETAILED DESCRIPTION
  • In the following description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following description of example embodiments is, therefore, not to be taken in a limited sense, and the scope of the present invention is defined by the appended claims.
  • The functions or algorithms described herein may be implemented in software or a combination of software and human or enterprise implemented procedures in one embodiment. The software may consist of computer executable instructions stored on computer readable media such as memory or other type of storage devices. Further, such functions correspond to modules, which are software, hardware, firmware, or any combination thereof. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples. The software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system.
  • As disclosed herein, one or more radar-based displacement sensors may be used to gather vibration information from an operating machine. The radar-based displacement sensor may be steered or directed toward multiple regions of interest on the operating machine to gather vibration information from the multiple regions of interest.
  • In one embodiment, the data produced from the one or more radar based displacement sensors may be processed in a condition monitoring system or other sensing system that allows monitoring of the entire machine and a plurality of sensing locations. In combination with mounted sensors and backend software to analyze the data, maintenance information and other monitoring data may be extracted from the sensing system and provided to a user for remedial action.
  • Despite an accelerometer's ability to monitor instrumented parts of the machine, relying on the use of accelerometers in a condition monitoring system or like sensing system may result in a number of location and design limitations (particularly for permanent sensor installation). Further, economic reasons may limit the locations where accelerometers can be installed and deployed.
  • Tethered accelerometer heads suffer the same limitations as its permanently installed counterpart. That is, tethered sensors need to make mechanical contact with the machine and often require supplemental measurements such as an optical tachometer. Handheld accelerometers can provide a wider range, but their accuracy depends on the skill level of the technician. Further, it may not be safe to approach the machine with an attachable handheld sensor and try to make the sensor head reach the remote location of interest.
  • Many engineers often would like access to the vibration data from an un-instrumented part of the machine, or would like the ability to change the areas of interest for monitoring. These limitations can be alleviated by use of a non-contact or stand-off measurement sensor in connection with the presently disclosed sensing system.
  • A sensing system (hardware and software) such as the example system illustrated in FIG. 1 may be used to monitor an entire machine with moving parts, using a scanning radar-based displacement sensor, permanently mounted sensors, and back-end software to enable maintenance related decision making. Structural problems can develop anywhere in the machine and are not restricted to the location where embedded sensors may be located. Collecting data with the use of scanning radar-based vibration sensors provides the ability to monitor large areas of the “entire” machine, while also analyzing well-known weak spots in the machine as necessary for rapid, accurate assessment of machine health.
  • Specifically, the presently disclosed sensing system may be deployed to monitor a wide variety of rotating machinery and mechanical components within a machine. This rotating machinery may include one or more of an electric motor, pulleys, gearboxes, shafts, bearings, drivelines, and like mechanical parts. Within such rotating machinery, abnormal harmonic readings may indicate rotational misbalance as a result from a failing or misbalanced component.
  • The sensing system may process these harmonic readings and other temporal and spectral data to detect the status of various operational components. The sensing system may specifically generate health indicators to alert users to warnings and failures such as stator breaks, rotor bending, wiring, shaft misalignment, gear tooth breaks, and bearing spalls. Use of the displacement vibration sensor within the sensing system provides a non-intrusive, non-contact approach to detect failures without adding conventional accelerometers. This not only makes it easy to retrofit legacy machines, but also allows the same apparatus to monitor more than one machine at the same time.
  • An example sensing system includes several components used to provide sensing measurements and direct operation of sensors as necessary. In one embodiment, the system may include a stand-off sensor that observes the machine for vibration induced displacements, one or more of permanently mounted sensors that provide measurement of machine condition at point locations, a steering system that steers the stand-off sensor to observe specific regions of interest within the machine, a software module that analyzes the data from various sensors, and a decision support module that calculates the machine health indicators and potential maintenance actions.
  • In one embodiment, the stand-off sensor is a radar based displacement sensor. For example, the stand-off sensor may use Doppler-based radar techniques to transmit and measure signals aimed at a specific object of interest. The stand-off sensor may be electronically or mechanically steered towards the specific object for monitoring. The steering may be either periodic or trigger-based, and driven by any combination of human and automated control. For example, a steering subsystem may be used to position an electromechanically operated phased-array radar antenna within the stand-off sensor.
  • In connection the presently described sensing system, a non-contact, radar-based sensor may be configured to sense vibration from a considerable distance (e.g., 4 feet), and provide a wide field of view that could be adjusted to monitor the entire machine or specific parts of the machine. Further, the sensor may be tuned to detect only the motion of the machinery and reject background vibration, providing vibration data from under-instrumented parts of the machine without incurring additional costs. This makes acquiring vibration data from previously un-instrumented parts not only cost-effective, but also safe.
  • Moreover, rejection of background vibration is inherent to the sensor because the radar sensor detects only the motion of the machinery relative to the sensor. The radar antenna of the sensor may also be configured to have a tunable or narrow field of view, such as providing no more than a 10-degree field of view and therefore could be deployed as a handheld or spot sensor.
  • In summary, a radar-based displacement sensor used in the presently disclosed sensing system transmits RF energy toward a target area to be monitored. The RF energy reflects from metal surfaces and edges within the target area and returns to the sensor. The sensor may be calibrated to reject any movement that is common to the target and the antenna. Further, the sensing system may process and analyze data from a plurality of displacement sensors or in combination with a plurality of fixed accelerometers to identify and reject invalid data. The data produced by the radar-based displacement sensor may be therefore refined or processed at the sensor or within a processing module separate to the sensor.
  • An embodiment of a sensor based system 100 is illustrated in FIG. 1. Machine 110 may contain a plurality of moving parts or components that are intended to be monitored to ensure proper mechanical operation. A series of accelerometers 121, 122, 123, 124 may be attached to or otherwise directly proximate to various moving components of the machine 110.
  • A radar sensor 130 may be positioned auxiliary to accelerometers 121, 122, 123, 124, or may be positioned as a portable stand alone sensor. For example, an operator may bring the radar sensor 130 to a factory floor and position it on a tripod at a distance (such as less than four feet) from the machine 110 when suspecting performance issues, or when the machine operation is in need of a period checkup. The sensor 130 may be configured to collect vibration or displacement data for a period of time, such as over a few days, allowing the data collected over this period of time to be analyzed for indicators of possible problems such as motor damage or wear.
  • Specifically, the radar sensor 130 transmits and receives radar energy 131 to and from the machine 110, collecting vibration data from the whole or a large part of the machine. The vibration data including temporal or spectral data collected from the combination of accelerometers 121, 122, 123, 124 and the radar sensor 130 is then transmitted to an analysis component 150 as represented by lines 141, 142, 144, 145, and 143 respectively. The lines may represent hardwired communication lines, or wireless connections in various embodiments.
  • A certain measured threshold of change, e.g., an amplitude of velocity or frequency shift/new frequency, may be configured to create or otherwise initiate a request for maintenance within the analysis component 150. Specifically, information from the radar sensor 130 may be collected as a time sampled data or Fast Fourier Transform (FFT) of the data or frequencies and amplitudes of the spectrum peaks. Information from different peaks can be combined, for example, to enable the velocity at the moving parts of the machine to be correlated with the load. The loading condition may be used for the interpretation of the spectral peaks corresponding to vibrations which may shift or broaden for different loads, such as may be measured from bearing vibrations.
  • FIG. 2 illustrates another embodiment providing a configuration of a sensor system 200 configured to monitor operation of multiple machines or components with use of a radar-based sensor. As illustrated, machine A 211 and machine B 212 are monitored with a series of permanently mounted sensors, such as accelerometers 221, 222, and 223, 224 respectively. The radar-based sensor 230 may configured to direct RF energy towards one or both of machine A 211 and machine B 212 as shown in 231 and 232. In one embodiment, a steering motion may be applied to the radar-based sensor 230 to aim the sensor and direct a single source of RF energy towards one of the machines at a single point in time. This steering motion may be communicated to the radar sensor through use of motion instructions 233. In other embodiments, the radar sensor 230 provides multiple sources of energy to achieve simultaneous monitoring of multiple machines at a single point in time.
  • The temporal or spectral data from the sensor 230 is then transmitted as represented at 243 to a data analysis component 240. Likewise, data from the mounted accelerometers 221, 222, 223, 224 is communicated as represented by lines 241, 242, 244, and 245. The data analysis component 250 may then fuse in the data from the variety of the permanently mounted sensors 221, 222, 223, 224 and the radar-based sensor 230 to determine the status of larger machine operations as a whole, or the status of specific machine subsystems and processes, based on the comparison of data from a plurality of sensors. This may result in the generation of user indications, such as one or more recommended maintenance actions 260, the generation of health indicators 270, or other useful machine-related information.
  • The radar-based sensor may be configured to detect machine vibration and misbalance (such that occurring from bearing damage) at a distance without making any contact with the machine. The data produced from these remote measurements may even exceed the performance of a high-quality screw accelerometer mounted directly on the bearing enclosure.
  • FIG. 3 provides an illustration of an example monitoring system using a combination of radar-based and permanently mounted sensors to monitor a mechanical system, specifically a horizontal axis wind turbine 300. As depicted, a number of sensors are depicted as being used to monitor the operation of the electrical generating components of the turbine 300 within its nacelle 302.
  • As shown, two radar-based vibration sensors 310, 312 are placed within the nacelle 302 and adjacent to the mechanical drive used to ultimately rotate generator 320. The first radar-based vibration sensor 310 is configured to monitor the high-speed shaft 322 located between generator 320 and gear box 324. Likewise the second radar-based vibration sensor 312 is configured to monitor the main shaft 330 located between transmission 326 and hub 334, specifically the portion of the main shaft extending between bearings 328 and 332.
  • Further, the data from multiple sensors such as radar-based vibration sensors 310, 312 may be compared with each other in the sensing system to compare relative vibration or other harmonics that are constant in operation of the system. The data collected from the radar-based vibration sensors 310, 312 may then be transmitted from the sensors to an off-set processing system, using any of a number of wired or wireless networking data connections.
  • FIG. 4 provides another illustration of use of the sensor-based system with a portable vibrometer embodiment, specifically in conjunction with a handheld inspection device. As shown, operator 400 aims a radar-based sensor 410 towards a mechanical system; illustrated here as a high-pressure pump 420 such as may be used in a large pump farm. The sensor 410 transmits and receives the RF radio signal 430 from one or more moving parts within the high pressure pump 420. The operator then uses some type of a readout display unit to assist with focusing the radar sensor and to obtain feedback.
  • As illustrated, the radio frequency data obtained at the radar sensor 410 may be transmitted to a computing device, here a portable handheld measurement device 440 having a display screen interface 450. In the depicted embodiment, the handheld measurement device 440 is able to process the RF signals obtained from sensor 410 and provide immediate feedback and measurements related to the sensor readings. However, the data may be transmitted from the radar sensor to another computing system, such as being transmitted from the handheld measurement device 440 using a network connection to an off-site computing system (not illustrated) for analysis and processing.
  • The handheld measurement device 440 may be configured to provide a visual indication 450 of the status of the sensor measurements to the operator 400. This visual indication may be presented as a graph rendered on a display screen, a textual indicator of measurements, or another illustration or indication rendered on the display screen. A variety of other graphical, textual, audio, or tactile indications may be provided to the user through the device 440 to indicate warnings, failures, and other malfunctions of the monitored equipment.
  • In one specific embodiment, the radar-based displacement sensor generates RF frequency at 24 GHz, an unregulated frequency band. The reflection phase changes proportionally to the displacement of the reflective surface relative to the radar divided by the RF frequency signal wavelength. The wavelength of the signal is short (e.g., 1.25 cm) for high sensitivity of the sensor. The reflected signal is modulated by the target vibration magnitude, such that any movement that is common to the target and the antenna is rejected.
  • Upon return to the sensor, the return signals are mixed (beat against each other) with transmitted signals. The output signal phase of the sensor follows the radial displacement (in a direction perpendicular to the antenna) of the target in the time domain. The output signal is converted in the frequency domain by Fast Fourier Transform (FFT). If the reflecting surfaces in the radar antenna's field of view move at different frequencies or amplitudes, they will contribute different spectral peaks in the sensor signal. Thus, one sensor with a wide field of view is capable of monitoring many moving parts at the same time.
  • The sensor, in some embodiments, may provide high sensitivity that decreases for longer distances, and may also provide an output signal that is decreasing with increased distance. For example, a sensor may be configured to detect displacement as small as 0.1 nm at a distance of 50 cm and 0.5 nm at a distance of 133.5 cm. The very high sensitivity is due to the very short round-trip time for the return signal (e.g., 3 nsec for 50 cm). Therefore, the local oscillator does not drift much and the phase noise of the sensor is very low. The round-trip time and thus the phase noise are larger for longer distances. The amplitude of the sensor decreases proportionally to the distance because the other half of the mixing energy comes from the local oscillator in the sensor and does not change with the distance. Therefore, large sensing distances are feasible for comparatively low transmission power (e.g., 50 mW).
  • The radar-based sensor may be used to detect higher harmonic side-bands and higher harmonics than that detected by an accelerometer. Moreover the signal-to-noise ratio (energy in the sidebands divided by the noise energy around the sidebands) may be higher for a radar-based sensor than in an accelerometer. A radar-based sensor can be tuned to wide angles or otherwise tuned to a specific field of view to monitor the entire machine or specific parts of the machine, while detecting the motion of the machinery and rejecting background vibration. Therefore, use of a radar-based sensor may have a higher probability of detecting misbalance faults than an accelerometer.
  • Use of the presently described radar-based sensor and sensing system may provide improvements over noncontact vibration sensing techniques based on laser technology. In some embodiments, the cost for the sensing components may be an order of magnitude lower. Some embodiments may be capable of wide field sensing with no moving parts. Further embodiments may penetrate nonconductive protection layers, and various embodiments may be made without the need for delicate optical components and connections.
  • The radar-based sensor and sensing system may be configured to reject the common mode vibration that may be obstructing the fault signature in machines as well as its insensitivity to surface fouling. For example, this limits optical sensors in weakly supported platforms such as a helicopter body or the gearbox in a wind turbine nacelle. In combination with the presently disclosed sensing system, warnings and other usable information can be generated and transmitted in a rapid fashion with significant advantages over existing sensing techniques.
  • A block diagram of an example computer system that performs data analysis from the various sensors, and includes a decision support module that calculates the machine health indicators and potential maintenance actions and executes other necessary analysis and programming is shown in FIG. 5. A general computing device in the form of a computer 510 may include a processing unit 502, memory 504, removable storage 512, and non-removable storage 514. Memory 504 may include volatile memory 506 and non-volatile memory 508. Computer 510 may include—or have access to a computing environment that includes—a variety of computer-readable media, such as volatile memory 506 and non-volatile memory 508, removable storage 512 and non-removable storage 514. Computer storage includes random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM) & electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium capable of storing computer-readable instructions. Computer 510 may include or have access to a computing environment that includes input 516, output 518, and a communication connection 520. The computer may operate in a networked environment using a communication connection to connect to one or more remote computers. The remote computer may include a personal computer (PC), server, router, network PC, mobile device, a peer device or other common network node, or the like. The communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN) or other networks.
  • Computer-readable instructions to execute methods and algorithms described above may be stored on a computer-readable medium such as illustrated at a program storage device 525 are executable by the processing unit 502 of the computer 510. A hard drive, CD-ROM, and RAM are some examples of articles including a computer-readable medium. In one embodiment, a user interface is provided in connection with the computer system, such as a touch screen device for providing both input 516 and output 518.
  • The presently described sensing system and radar-based sensors may be implemented in a variety of settings, including but not limited to industrial and aerospace applications. Those skilled in the art would recognize that variations to the presently described embodiments may be used to apply the presently disclosed techniques to a variety of other mechanical and electromechanical applications and fields of use.

Claims (20)

1. A sensing system comprising:
a radar-based vibration sensor, the radar-based vibration sensor configured to obtain vibration data from mechanical operation of a component in a machine of interest; and
a processing unit configured to analyze the vibration data obtained by the radar-based vibration sensor, and provide indications related to a status of the mechanical operation of the component in the machine of interest.
2. The system of claim 1, wherein the radar-based vibration sensor is a displacement sensor transmitting radar signals using Doppler radar techniques, and wherein the vibration data includes temporal and spectral data related to the mechanical operation of the component.
3. The system of claim 1, wherein the radar-based vibration sensor is configured to detect vibrations from a plurality of different locations of the mechanical operation for the machine of interest.
4. The system of claim 1, wherein the radar-based vibration sensor provides a tunable field of view used to focus on a specific area of the mechanical operation for the machine of interest.
5. The system of claim 1, wherein the processing unit is operably coupled to a decision support module configured to calculate at least one of machine health indicators and potential maintenance actions in connection with the indications related to the status of the mechanical operation of the component.
6. The system of claim 1, further comprising:
a plurality of vibration sensors mounted to the machine of interest, the machine mounted vibration sensors configured to obtain vibration data from mechanical operation of components proximate to locations of mounting; and
wherein the processing unit is further configured to analyze the data provided by the machine mounted vibration sensors, and fuse the data provided by the machine mounted vibration sensors with the information from the radar-based vibration sensor.
7. The system of claim 6, further comprising:
one or more additional radar-based vibration sensors configured to obtain vibration data from mechanical operation of additional components in the machine of interest;
wherein the processing unit is further configured to analyze the data provided by the one or more additional radar-based vibration sensors, and fuse the data provided by the one or more additional radar-based vibration sensors with the information from the machine mounted vibration sensors and the radar-based vibration sensor; and
wherein at least one of the machine mounted vibration sensors is an accelerometer.
8. A sensing system comprising:
a radar-based displacement sensor configured to collect vibration data, the vibration data including temporal and spectral data;
a steering system configured to direct the radar-based displacement sensor to collect the vibration data from different locations of mechanical operation for a machine of interest; and
a processing system configured to analyze the data collected by the radar-based vibration sensor, and provide user indications related to status of the mechanical operation for the machine of interest.
9. The system of claim 8, wherein the steering system directs the radar-based displacement sensor to focus radar energy at predetermined locations of the machine of interest, causing monitoring of specific components of the machine of interest.
10. The system of claim 8, wherein the steering system directs the radar-based displacement sensor to focus radar energy at additional locations of mechanical operation periodically.
11. The system of claim 8, wherein the steering system directs the radar-based displacement sensor to focus radar energy at additional locations of mechanical operation based on sensed vibrations determined within the processing system.
12. The system of claim 8, wherein the processing unit is operably coupled to a decision support module configured to calculate at least one of machine health indicators and potential maintenance actions.
13. The system of claim 8, further comprising:
multiple machine mounted vibration sensors configured to provide vibration information from selected regions where such sensors are mounted;
wherein the processing unit is further configured to analyze the data provided by the multiple machine mounted vibration sensors, and fuse the data provided by the multiple machine mounted vibration sensors with the vibration data collected from the radar-based displacement sensor.
14. The system of claim 13, further comprising:
one or more additional radar-based displacement sensors configured to collect vibration data;
wherein the processing unit is further configured to analyze the data provided by the one or more additional radar-based displacement sensors, and fuse the data provided by the one or more additional radar-based displacement sensors with the information from the machine mounted vibration sensors and the radar-based displacement sensor; and
wherein at least one machine mounted sensor is an accelerometer.
15. A method performed by a sensing system, comprising:
collecting vibration data from a radar-based displacement sensor, the radar-based displacement sensor being directed to measure vibrations from a region of interest on an operating machine, and the vibration data including one or both of temporal and spectral data;
processing the vibration data to analyze vibration measurements from the radar-based displacement sensor and derive a status of mechanical operation for the operating machine; and
providing user indications related to the status of the mechanical operation for the operating machine.
16. The method of claim 15, further comprising:
transmitting motion instructions to a steering mechanism for the radar-based displacement sensor to steer the radar-based displacement sensor and focus radar energy at a different region of interest, thereby collecting vibration information from the different region of interest on the operating machine.
17. The method of claim 16, wherein the motion instructions provided to steer the radar-based displacement sensor to focus radar energy at a different region of interest are determined based on a predetermined schedule or the status of the mechanical operation for the operating machine.
18. The method of claim 15, further comprising:
evaluating the status of the mechanical operation for the operating machine to produce one or both of suggested maintenance actions and machine health indicators; and
providing the one or both of suggested maintenance actions and machine health indicators in connection with the user indications.
19. The method of claim 15, further comprising:
collecting vibration data from mounted vibration sensors configured to measure vibrations from a region of interest proximate to the mounted vibration sensors on the operating machine; and
processing the vibration data collected from the mounted vibration sensors and combining the vibration data collected from the mounted vibration sensors with the vibration data collected from the radar-based displacement sensor.
20. The method of claim 19, further comprising:
collecting vibration data from one or more additional radar-based displacement sensors;
processing the vibration data collected from the one or more additional radar-based displacement sensors, including combining the vibration data collected from the one or more additional radar-based displacement sensors with the vibration data collected from the mounted vibration sensors and the radar-based displacement sensor; and
wherein the mounted vibration sensors comprise a plurality of accelerometers.
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Cited By (18)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110283768A1 (en) * 2010-05-24 2011-11-24 Honeywell International Inc. Self-calibrating vibration sensor
US20140095114A1 (en) * 2012-09-28 2014-04-03 Hubertus V. Thomeer System And Method For Tracking And Displaying Equipment Operations Data
US9739685B2 (en) 2014-04-15 2017-08-22 International Business Machines Corporation Integrated, predictive vibration analysis of rotational machine within electronics rack
WO2018094273A1 (en) * 2016-11-17 2018-05-24 Ez Pulley Llc Systems and methods for detection and analysis of faulty components in a rotating pulley system
US20180284743A1 (en) * 2016-05-09 2018-10-04 StrongForce IoT Portfolio 2016, LLC Methods and systems for industrial internet of things data collection for vibration sensitive equipment
US20190228636A1 (en) * 2018-01-23 2019-07-25 Computational Systems, Inc. Vibrational analysis systems and methods
US10678233B2 (en) 2017-08-02 2020-06-09 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and data sharing in an industrial environment
CN111609920A (en) * 2020-05-13 2020-09-01 上海交通大学 Handheld Microwave Vibration Measurement System
US10914819B2 (en) * 2018-08-02 2021-02-09 GM Global Technology Operations LLC Mitigating vibration in a radar system on a moving platform
US10983507B2 (en) 2016-05-09 2021-04-20 Strong Force Iot Portfolio 2016, Llc Method for data collection and frequency analysis with self-organization functionality
US11016003B2 (en) 2016-11-17 2021-05-25 Ez Pulley Llc Systems and methods for detection and analysis of faulty components in a rotating pulley system
US11199835B2 (en) 2016-05-09 2021-12-14 Strong Force Iot Portfolio 2016, Llc Method and system of a noise pattern data marketplace in an industrial environment
US11199837B2 (en) 2017-08-02 2021-12-14 Strong Force Iot Portfolio 2016, Llc Data monitoring systems and methods to update input channel routing in response to an alarm state
US11237546B2 (en) 2016-06-15 2022-02-01 Strong Force loT Portfolio 2016, LLC Method and system of modifying a data collection trajectory for vehicles
CN115754941A (en) * 2022-11-14 2023-03-07 扬州宇安电子科技有限公司 Distributed radar running state monitoring system and method based on artificial intelligence
US11774944B2 (en) 2016-05-09 2023-10-03 Strong Force Iot Portfolio 2016, Llc Methods and systems for the industrial internet of things
EP4290196A1 (en) 2022-06-09 2023-12-13 Rosemount Tank Radar AB Vibration monitoring system and method
US12276420B2 (en) 2016-02-03 2025-04-15 Strong Force Iot Portfolio 2016, Llc Industrial internet of things smart heating systems and methods that produce and use hydrogen fuel

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6468353B2 (en) * 2015-05-14 2019-02-13 富士通株式会社 Air conditioner, sensor unit, and control system and control method for air conditioner
WO2020126051A1 (en) * 2018-12-21 2020-06-25 Telefonaktiebolaget Lm Ericsson (Publ) Method for improving radar measurements in a handheld device
CN110726978A (en) * 2019-11-29 2020-01-24 北京无线电测量研究所 A radar health state maintenance method, system, medium and equipment
CN111323112A (en) * 2020-04-03 2020-06-23 宜春学院 Wireless monitoring device for rotary mechanical vibration

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4887087A (en) * 1982-03-16 1989-12-12 Micro Control Technology Limited Method of displaying detected information about a rotating mass
US20030014199A1 (en) * 2001-07-12 2003-01-16 Patrick Toomey System and methods for detecting fault in structure
US6672167B2 (en) * 2001-04-23 2004-01-06 The Aerospace Corporation Method and system for processing laser vibrometry data employing bayesian statistical processing techniques
US6972846B2 (en) * 2003-03-31 2005-12-06 Metrolaser, Inc. Multi-beam heterodyne laser doppler vibrometer
US20090043441A1 (en) * 1995-06-07 2009-02-12 Automotive Technologies International, Inc. Information Management and Monitoring System and Method

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5159406A (en) 1964-09-28 1992-10-27 Zenith Electronics Corporation Light-operated accelerometer-type techniques
US5760731A (en) 1995-12-19 1998-06-02 Fisher Controls International, Inc. Sensors and methods for sensing displacement using radar
US6621561B2 (en) 2000-09-22 2003-09-16 Virginia Tech Intellectual Properties Doppler rotational velocity sensor
US6489917B2 (en) 2000-11-30 2002-12-03 Georgia Tech Research Corporation Phase-based sensing system
US6750621B2 (en) * 2001-09-10 2004-06-15 Sauer-Danfoss Inc. Method and system for non-contact sensing of motion of a roller drum
US7533572B2 (en) * 2006-08-15 2009-05-19 Siemens Energy, Inc. High bandwidth fiber optic vibration sensor
US7405814B2 (en) * 2006-12-19 2008-07-29 The Boeing Company Frequency multiplexed, multiple channel heterodyne interferometer

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4887087A (en) * 1982-03-16 1989-12-12 Micro Control Technology Limited Method of displaying detected information about a rotating mass
US4887087B1 (en) * 1982-03-16 1992-03-24 Micro Control Tech Ltd
US20090043441A1 (en) * 1995-06-07 2009-02-12 Automotive Technologies International, Inc. Information Management and Monitoring System and Method
US6672167B2 (en) * 2001-04-23 2004-01-06 The Aerospace Corporation Method and system for processing laser vibrometry data employing bayesian statistical processing techniques
US20030014199A1 (en) * 2001-07-12 2003-01-16 Patrick Toomey System and methods for detecting fault in structure
US6972846B2 (en) * 2003-03-31 2005-12-06 Metrolaser, Inc. Multi-beam heterodyne laser doppler vibrometer

Cited By (123)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110283768A1 (en) * 2010-05-24 2011-11-24 Honeywell International Inc. Self-calibrating vibration sensor
US8746035B2 (en) * 2010-05-24 2014-06-10 Honeywell International Inc. Self-calibrating vibration sensor
US20140095114A1 (en) * 2012-09-28 2014-04-03 Hubertus V. Thomeer System And Method For Tracking And Displaying Equipment Operations Data
US9739685B2 (en) 2014-04-15 2017-08-22 International Business Machines Corporation Integrated, predictive vibration analysis of rotational machine within electronics rack
US9915584B2 (en) 2014-04-15 2018-03-13 International Business Machines Corporation Integrated, predictive vibration analysis of rotational machine within electronics rack
US12276420B2 (en) 2016-02-03 2025-04-15 Strong Force Iot Portfolio 2016, Llc Industrial internet of things smart heating systems and methods that produce and use hydrogen fuel
US11262737B2 (en) 2016-05-09 2022-03-01 Strong Force Iot Portfolio 2016, Llc Systems and methods for monitoring a vehicle steering system
US11507064B2 (en) 2016-05-09 2022-11-22 Strong Force Iot Portfolio 2016, Llc Methods and systems for industrial internet of things data collection in downstream oil and gas environment
US12259711B2 (en) 2016-05-09 2025-03-25 Strong Force Iot Portfolio 2016, Llc Methods and systems for the industrial internet of things
US12244359B2 (en) 2016-05-09 2025-03-04 Strong Force Iot Portfolio 2016, Llc Systems and methods for monitoring pumps and fans
US10712738B2 (en) * 2016-05-09 2020-07-14 Strong Force Iot Portfolio 2016, Llc Methods and systems for industrial internet of things data collection for vibration sensitive equipment
US10732621B2 (en) 2016-05-09 2020-08-04 Strong Force Iot Portfolio 2016, Llc Methods and systems for process adaptation in an internet of things downstream oil and gas environment
US10754334B2 (en) 2016-05-09 2020-08-25 Strong Force Iot Portfolio 2016, Llc Methods and systems for industrial internet of things data collection for process adjustment in an upstream oil and gas environment
US12237873B2 (en) 2016-05-09 2025-02-25 Strong Force Iot Portfolio 2016, Llc Systems and methods for balancing remote oil and gas equipment
US12191926B2 (en) 2016-05-09 2025-01-07 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with noise detection and system response for vibrating components
US12140930B2 (en) 2016-05-09 2024-11-12 Strong Force Iot Portfolio 2016, Llc Method for determining service event of machine from sensor data
US10866584B2 (en) 2016-05-09 2020-12-15 Strong Force Iot Portfolio 2016, Llc Methods and systems for data processing in an industrial internet of things data collection environment with large data sets
US12099911B2 (en) 2016-05-09 2024-09-24 Strong Force loT Portfolio 2016, LLC Systems and methods for learning data patterns predictive of an outcome
US12079701B2 (en) 2016-05-09 2024-09-03 Strong Force Iot Portfolio 2016, Llc System, methods and apparatus for modifying a data collection trajectory for conveyors
US12039426B2 (en) 2016-05-09 2024-07-16 Strong Force Iot Portfolio 2016, Llc Methods for self-organizing data collection, distribution and storage in a distribution environment
US10983507B2 (en) 2016-05-09 2021-04-20 Strong Force Iot Portfolio 2016, Llc Method for data collection and frequency analysis with self-organization functionality
US10983514B2 (en) 2016-05-09 2021-04-20 Strong Force Iot Portfolio 2016, Llc Methods and systems for equipment monitoring in an Internet of Things mining environment
US11003179B2 (en) 2016-05-09 2021-05-11 Strong Force Iot Portfolio 2016, Llc Methods and systems for a data marketplace in an industrial internet of things environment
US11009865B2 (en) 2016-05-09 2021-05-18 Strong Force Iot Portfolio 2016, Llc Methods and systems for a noise pattern data marketplace in an industrial internet of things environment
US11996900B2 (en) 2016-05-09 2024-05-28 Strong Force Iot Portfolio 2016, Llc Systems and methods for processing data collected in an industrial environment using neural networks
US11029680B2 (en) 2016-05-09 2021-06-08 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with frequency band adjustments for diagnosing oil and gas production equipment
US11838036B2 (en) 2016-05-09 2023-12-05 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment
US11048248B2 (en) 2016-05-09 2021-06-29 Strong Force Iot Portfolio 2016, Llc Methods and systems for industrial internet of things data collection in a network sensitive mining environment
US11054817B2 (en) 2016-05-09 2021-07-06 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection and intelligent process adjustment in an industrial environment
US11836571B2 (en) 2016-05-09 2023-12-05 Strong Force Iot Portfolio 2016, Llc Systems and methods for enabling user selection of components for data collection in an industrial environment
US11073826B2 (en) 2016-05-09 2021-07-27 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection providing a haptic user interface
US11086311B2 (en) 2016-05-09 2021-08-10 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection having intelligent data collection bands
US11092955B2 (en) 2016-05-09 2021-08-17 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection utilizing relative phase detection
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US11112785B2 (en) 2016-05-09 2021-09-07 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and signal conditioning in an industrial environment
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US11119473B2 (en) 2016-05-09 2021-09-14 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and processing with IP front-end signal conditioning
US11126171B2 (en) 2016-05-09 2021-09-21 Strong Force Iot Portfolio 2016, Llc Methods and systems of diagnosing machine components using neural networks and having bandwidth allocation
US11797821B2 (en) 2016-05-09 2023-10-24 Strong Force Iot Portfolio 2016, Llc System, methods and apparatus for modifying a data collection trajectory for centrifuges
US11791914B2 (en) 2016-05-09 2023-10-17 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial Internet of Things data collection environment with a self-organizing data marketplace and notifications for industrial processes
US11137752B2 (en) 2016-05-09 2021-10-05 Strong Force loT Portfolio 2016, LLC Systems, methods and apparatus for data collection and storage according to a data storage profile
US11774944B2 (en) 2016-05-09 2023-10-03 Strong Force Iot Portfolio 2016, Llc Methods and systems for the industrial internet of things
US11156998B2 (en) 2016-05-09 2021-10-26 Strong Force Iot Portfolio 2016, Llc Methods and systems for process adjustments in an internet of things chemical production process
US11169511B2 (en) 2016-05-09 2021-11-09 Strong Force Iot Portfolio 2016, Llc Methods and systems for network-sensitive data collection and intelligent process adjustment in an industrial environment
US11770196B2 (en) 2016-05-09 2023-09-26 Strong Force TX Portfolio 2018, LLC Systems and methods for removing background noise in an industrial pump environment
US11181893B2 (en) 2016-05-09 2021-11-23 Strong Force Iot Portfolio 2016, Llc Systems and methods for data communication over a plurality of data paths
US11194319B2 (en) 2016-05-09 2021-12-07 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection in a vehicle steering system utilizing relative phase detection
US11194318B2 (en) 2016-05-09 2021-12-07 Strong Force Iot Portfolio 2016, Llc Systems and methods utilizing noise analysis to determine conveyor performance
US11199835B2 (en) 2016-05-09 2021-12-14 Strong Force Iot Portfolio 2016, Llc Method and system of a noise pattern data marketplace in an industrial environment
US11755878B2 (en) 2016-05-09 2023-09-12 Strong Force Iot Portfolio 2016, Llc Methods and systems of diagnosing machine components using analog sensor data and neural network
US11728910B2 (en) 2016-05-09 2023-08-15 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with expert systems to predict failures and system state for slow rotating components
US11215980B2 (en) 2016-05-09 2022-01-04 Strong Force Iot Portfolio 2016, Llc Systems and methods utilizing routing schemes to optimize data collection
US11221613B2 (en) 2016-05-09 2022-01-11 Strong Force Iot Portfolio 2016, Llc Methods and systems for noise detection and removal in a motor
US11663442B2 (en) 2016-05-09 2023-05-30 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial Internet of Things data collection environment with intelligent data management for industrial processes including sensors
US11646808B2 (en) 2016-05-09 2023-05-09 Strong Force Iot Portfolio 2016, Llc Methods and systems for adaption of data storage and communication in an internet of things downstream oil and gas environment
US11243521B2 (en) 2016-05-09 2022-02-08 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection in an industrial environment with haptic feedback and data communication and bandwidth control
US11243528B2 (en) 2016-05-09 2022-02-08 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection utilizing adaptive scheduling of a multiplexer
US11243522B2 (en) 2016-05-09 2022-02-08 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial Internet of Things data collection environment with intelligent data collection and equipment package adjustment for a production line
US11256243B2 (en) 2016-05-09 2022-02-22 Strong Force loT Portfolio 2016, LLC Methods and systems for detection in an industrial Internet of Things data collection environment with intelligent data collection and equipment package adjustment for fluid conveyance equipment
US11256242B2 (en) 2016-05-09 2022-02-22 Strong Force Iot Portfolio 2016, Llc Methods and systems of chemical or pharmaceutical production line with self organizing data collectors and neural networks
US20180284743A1 (en) * 2016-05-09 2018-10-04 StrongForce IoT Portfolio 2016, LLC Methods and systems for industrial internet of things data collection for vibration sensitive equipment
US11269318B2 (en) 2016-05-09 2022-03-08 Strong Force Iot Portfolio 2016, Llc Systems, apparatus and methods for data collection utilizing an adaptively controlled analog crosspoint switch
US11269319B2 (en) 2016-05-09 2022-03-08 Strong Force Iot Portfolio 2016, Llc Methods for determining candidate sources of data collection
US11281202B2 (en) 2016-05-09 2022-03-22 Strong Force Iot Portfolio 2016, Llc Method and system of modifying a data collection trajectory for bearings
US11609552B2 (en) 2016-05-09 2023-03-21 Strong Force Iot Portfolio 2016, Llc Method and system for adjusting an operating parameter on a production line
US11397422B2 (en) 2016-05-09 2022-07-26 Strong Force Iot Portfolio 2016, Llc System, method, and apparatus for changing a sensed parameter group for a mixer or agitator
US11609553B2 (en) 2016-05-09 2023-03-21 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and frequency evaluation for pumps and fans
US11340589B2 (en) 2016-05-09 2022-05-24 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial Internet of Things data collection environment with expert systems diagnostics and process adjustments for vibrating components
US11347215B2 (en) 2016-05-09 2022-05-31 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with intelligent management of data selection in high data volume data streams
US11347206B2 (en) 2016-05-09 2022-05-31 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection in a chemical or pharmaceutical production process with haptic feedback and control of data communication
US11347205B2 (en) 2016-05-09 2022-05-31 Strong Force Iot Portfolio 2016, Llc Methods and systems for network-sensitive data collection and process assessment in an industrial environment
US11353850B2 (en) 2016-05-09 2022-06-07 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and signal evaluation to determine sensor status
US11353852B2 (en) 2016-05-09 2022-06-07 Strong Force Iot Portfolio 2016, Llc Method and system of modifying a data collection trajectory for pumps and fans
US11353851B2 (en) 2016-05-09 2022-06-07 Strong Force Iot Portfolio 2016, Llc Systems and methods of data collection monitoring utilizing a peak detection circuit
US11360459B2 (en) 2016-05-09 2022-06-14 Strong Force Iot Portfolio 2016, Llc Method and system for adjusting an operating parameter in a marginal network
US11366455B2 (en) 2016-05-09 2022-06-21 Strong Force Iot Portfolio 2016, Llc Methods and systems for optimization of data collection and storage using 3rd party data from a data marketplace in an industrial internet of things environment
US11366456B2 (en) 2016-05-09 2022-06-21 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with intelligent data management for industrial processes including analog sensors
US11372394B2 (en) 2016-05-09 2022-06-28 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial internet of things data collection environment with self-organizing expert system detection for complex industrial, chemical process
US11372395B2 (en) 2016-05-09 2022-06-28 Strong Force Iot Portfolio 2016, Llc Methods and systems for detection in an industrial Internet of Things data collection environment with expert systems diagnostics for vibrating components
US11378938B2 (en) 2016-05-09 2022-07-05 Strong Force Iot Portfolio 2016, Llc System, method, and apparatus for changing a sensed parameter group for a pump or fan
US11385622B2 (en) 2016-05-09 2022-07-12 Strong Force Iot Portfolio 2016, Llc Systems and methods for characterizing an industrial system
US11385623B2 (en) 2016-05-09 2022-07-12 Strong Force Iot Portfolio 2016, Llc Systems and methods of data collection and analysis of data from a plurality of monitoring devices
US11392116B2 (en) 2016-05-09 2022-07-19 Strong Force Iot Portfolio 2016, Llc Systems and methods for self-organizing data collection based on production environment parameter
US11392111B2 (en) 2016-05-09 2022-07-19 Strong Force Iot Portfolio 2016, Llc Methods and systems for intelligent data collection for a production line
US11392109B2 (en) 2016-05-09 2022-07-19 Strong Force Iot Portfolio 2016, Llc Methods and systems for data collection in an industrial refining environment with haptic feedback and data storage control
US11327475B2 (en) 2016-05-09 2022-05-10 Strong Force Iot Portfolio 2016, Llc Methods and systems for intelligent collection and analysis of vehicle data
US11586181B2 (en) 2016-05-09 2023-02-21 Strong Force Iot Portfolio 2016, Llc Systems and methods for adjusting process parameters in a production environment
US11397421B2 (en) 2016-05-09 2022-07-26 Strong Force Iot Portfolio 2016, Llc Systems, devices and methods for bearing analysis in an industrial environment
US11402826B2 (en) 2016-05-09 2022-08-02 Strong Force Iot Portfolio 2016, Llc Methods and systems of industrial production line with self organizing data collectors and neural networks
US11409266B2 (en) 2016-05-09 2022-08-09 Strong Force Iot Portfolio 2016, Llc System, method, and apparatus for changing a sensed parameter group for a motor
US11415978B2 (en) 2016-05-09 2022-08-16 Strong Force Iot Portfolio 2016, Llc Systems and methods for enabling user selection of components for data collection in an industrial environment
US11334063B2 (en) 2016-05-09 2022-05-17 Strong Force Iot Portfolio 2016, Llc Systems and methods for policy automation for a data collection system
US11493903B2 (en) 2016-05-09 2022-11-08 Strong Force Iot Portfolio 2016, Llc Methods and systems for a data marketplace in a conveyor environment
US11507075B2 (en) 2016-05-09 2022-11-22 Strong Force Iot Portfolio 2016, Llc Method and system of a noise pattern data marketplace for a power station
US11307565B2 (en) 2016-05-09 2022-04-19 Strong Force Iot Portfolio 2016, Llc Method and system of a noise pattern data marketplace for motors
US11573558B2 (en) 2016-05-09 2023-02-07 Strong Force Iot Portfolio 2016, Llc Methods and systems for sensor fusion in a production line environment
US11573557B2 (en) 2016-05-09 2023-02-07 Strong Force Iot Portfolio 2016, Llc Methods and systems of industrial processes with self organizing data collectors and neural networks
US11586188B2 (en) 2016-05-09 2023-02-21 Strong Force Iot Portfolio 2016, Llc Methods and systems for a data marketplace for high volume industrial processes
US11237546B2 (en) 2016-06-15 2022-02-01 Strong Force loT Portfolio 2016, LLC Method and system of modifying a data collection trajectory for vehicles
WO2018094273A1 (en) * 2016-11-17 2018-05-24 Ez Pulley Llc Systems and methods for detection and analysis of faulty components in a rotating pulley system
US11016003B2 (en) 2016-11-17 2021-05-25 Ez Pulley Llc Systems and methods for detection and analysis of faulty components in a rotating pulley system
US10908602B2 (en) 2017-08-02 2021-02-02 Strong Force Iot Portfolio 2016, Llc Systems and methods for network-sensitive data collection
US10824140B2 (en) 2017-08-02 2020-11-03 Strong Force Iot Portfolio 2016, Llc Systems and methods for network-sensitive data collection
US11231705B2 (en) 2017-08-02 2022-01-25 Strong Force Iot Portfolio 2016, Llc Methods for data monitoring with changeable routing of input channels
US11209813B2 (en) 2017-08-02 2021-12-28 Strong Force Iot Portfolio 2016, Llc Data monitoring systems and methods to update input channel routing in response to an alarm state
US11199837B2 (en) 2017-08-02 2021-12-14 Strong Force Iot Portfolio 2016, Llc Data monitoring systems and methods to update input channel routing in response to an alarm state
US11175653B2 (en) 2017-08-02 2021-11-16 Strong Force Iot Portfolio 2016, Llc Systems for data collection and storage including network evaluation and data storage profiles
US11144047B2 (en) 2017-08-02 2021-10-12 Strong Force Iot Portfolio 2016, Llc Systems for data collection and self-organizing storage including enhancing resolution
US11131989B2 (en) 2017-08-02 2021-09-28 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection including pattern recognition
US11397428B2 (en) 2017-08-02 2022-07-26 Strong Force Iot Portfolio 2016, Llc Self-organizing systems and methods for data collection
US10678233B2 (en) 2017-08-02 2020-06-09 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection and data sharing in an industrial environment
US11067976B2 (en) 2017-08-02 2021-07-20 Strong Force Iot Portfolio 2016, Llc Data collection systems having a self-sufficient data acquisition box
US10795350B2 (en) 2017-08-02 2020-10-06 Strong Force Iot Portfolio 2016, Llc Systems and methods for data collection including pattern recognition
US11442445B2 (en) 2017-08-02 2022-09-13 Strong Force Iot Portfolio 2016, Llc Data collection systems and methods with alternate routing of input channels
US10921801B2 (en) 2017-08-02 2021-02-16 Strong Force loT Portfolio 2016, LLC Data collection systems and methods for updating sensed parameter groups based on pattern recognition
US11036215B2 (en) 2017-08-02 2021-06-15 Strong Force Iot Portfolio 2016, Llc Data collection systems with pattern analysis for an industrial environment
US11126173B2 (en) 2017-08-02 2021-09-21 Strong Force Iot Portfolio 2016, Llc Data collection systems having a self-sufficient data acquisition box
US20190228636A1 (en) * 2018-01-23 2019-07-25 Computational Systems, Inc. Vibrational analysis systems and methods
US10607470B2 (en) * 2018-01-23 2020-03-31 Computational Systems, Inc. Vibrational analysis systems and methods
US10914819B2 (en) * 2018-08-02 2021-02-09 GM Global Technology Operations LLC Mitigating vibration in a radar system on a moving platform
CN111609920A (en) * 2020-05-13 2020-09-01 上海交通大学 Handheld Microwave Vibration Measurement System
EP4290196A1 (en) 2022-06-09 2023-12-13 Rosemount Tank Radar AB Vibration monitoring system and method
CN115754941A (en) * 2022-11-14 2023-03-07 扬州宇安电子科技有限公司 Distributed radar running state monitoring system and method based on artificial intelligence

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