WO2022102113A1 - 電動機の診断装置 - Google Patents

電動機の診断装置 Download PDF

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Publication number
WO2022102113A1
WO2022102113A1 PCT/JP2020/042570 JP2020042570W WO2022102113A1 WO 2022102113 A1 WO2022102113 A1 WO 2022102113A1 JP 2020042570 W JP2020042570 W JP 2020042570W WO 2022102113 A1 WO2022102113 A1 WO 2022102113A1
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WO
WIPO (PCT)
Prior art keywords
unit
motor
value
rotation frequency
calculation unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/JP2020/042570
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English (en)
French (fr)
Japanese (ja)
Inventor
裕登 安原
俊彦 宮内
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Mitsubishi Electric Corp
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Mitsubishi Electric Corp
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mitsubishi Electric Corp filed Critical Mitsubishi Electric Corp
Priority to JP2022561236A priority Critical patent/JP7422896B2/ja
Priority to KR1020237014758A priority patent/KR102914035B1/ko
Priority to CN202080107078.7A priority patent/CN116490760B/zh
Priority to DE112020007771.3T priority patent/DE112020007771T5/de
Priority to PCT/JP2020/042570 priority patent/WO2022102113A1/ja
Publication of WO2022102113A1 publication Critical patent/WO2022102113A1/ja
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M15/00—Testing of engines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/0046—Arrangements for measuring currents or voltages or for indicating presence or sign thereof characterised by a specific application or detail not covered by any other subgroup of G01R19/00
    • G01R19/0053—Noise discrimination; Analog sampling; Measuring transients
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
    • G01R19/165—Indicating that current or voltage is either above or below a predetermined value or within or outside a predetermined range of values
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/16—Spectrum analysis; Fourier analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
    • G01R35/005—Calibrating; Standards or reference devices, e.g. voltage or resistance standards, "golden" references
    • G01R35/007—Standards or reference devices, e.g. voltage or resistance standards, "golden references"

Definitions

  • the present application relates to a motor diagnostic device for diagnosing the presence or absence of an abnormality in a motor driven by an inverter.
  • inverter drive method a method of driving an electric motor by an inverter
  • inverter drive method The range in which inverter-driven motors are used is the power of production line equipment and machinery in the process industry. For example, pumps, compressors, blowers, industrial robots, etc. have a wide range of uses, and demand is on the rise. Therefore, such an electric motor is always required to have a sound continuous operation.
  • not all motors are operated in an appropriate usage environment, and it is not uncommon for them to operate in a high stress environment such as high temperature, high humidity, heavy load, corrosion, and wear.
  • TBM Time Based Maintenance
  • CBM Condition Based Maintenance
  • Patent Document 1 the pattern of the power spectral density obtained by performing Fourier analysis on various signals indicating the states of rotating devices such as pumps and electric motors is compared with the reference patterns of various signals in the normal state, and the reference patterns are compared.
  • a device that determines the presence or absence of an abnormality based on the distance from the device is disclosed.
  • the state of the inverter-driven motor can be diagnosed from information such as current and voltage that can be measured in the past at the motor control center, and reliability and productivity can be achieved.
  • a device to ensure safety is required.
  • the rotation speed or operating load fluctuates from moment to moment depending on the operation of the driver. Whether the condition of the motor is an event caused by the operating condition or an event caused by deterioration or failure because the parameters such as the current value or the voltage value required for diagnosis fluctuate with the fluctuation of the rotation speed or the operating load. It was difficult to judge.
  • the abnormality detection device for rotating equipment disclosed in Patent Document 1 may erroneously detect a change in operating conditions peculiar to an inverter drive system as deterioration of a motor when considering operation in a real environment. Further, in Patent Document 1, the threshold value for diagnosis is compared with that given in advance, and there is a possibility of erroneous detection due to variation in the spectral value of the rotation frequency of the motor depending on the installation condition of the motor.
  • This application has been made to solve the above-mentioned problems, and it is possible to detect changes in the operating conditions peculiar to the inverter drive system separately from the signs of deterioration of the motor, and to improve the detection accuracy. It is also possible to obtain a motor diagnostic device capable of preventing erroneous detection of motor diagnosis by detecting the installation status of the motor as a variation in the spectral value of the rotation frequency of the motor.
  • the electric motor diagnostic device disclosed in the present application includes a measurement circuit that inputs the current and voltage of the electric motor driven by an inverter, a sampling frequency calculation unit that determines the sampling frequency when the current is stable, and a current stable state.
  • the FFT analysis unit that frequency-analyzes the current of the electric machine at this time, the peak detection calculation unit that detects the peak part of the power spectrum analyzed by the FFT analysis unit, and the peak caused by the rotation frequency of the electric machine from the peak part of the power spectrum.
  • the rotation frequency band detection unit that obtains the location, the rotation frequency spectrum value detection unit that calculates the spectrum value of the peak location due to the rotation frequency of the electric motor, and the rotation frequency spectrum value detection unit's spectral values are moved and averaged multiple times.
  • Rotation frequency spectrum value moving average buffer for carrying out, an averaging calculation unit that averages the power spectra for multiple times, and a rotation frequency that calculates the variation of the spectrum value of the rotation frequency in the rotation frequency spectrum value moving average buffer.
  • Normal state storage that stores the calculation result of the ⁇ value calculation unit, the threshold calculation unit that calculates the threshold for determining the abnormality of the electric motor based on the calculation result of the rotation frequency ⁇ value calculation unit, and the calculation result of the averaging calculation unit as the normal state of the electric motor.
  • a load factor calculation unit that calculates the operating load factor of the electric motor, an FFT analysis result correction unit that corrects the FFT analysis result by the FFT analysis unit at the time of diagnosis, and a correction value that stores the correction value according to the operating load factor.
  • the data storage unit the FFT result correction matrix selection unit that selects the correction value of the FFT analysis result according to the values of the operating frequency and operating load factor of the electric motor, and the correction value and abnormality judgment of the electric motor in the FFT result correction matrix selection unit. It is provided with an abnormal state comparison unit that determines the operating status of the electric motor based on the threshold value to be performed.
  • the diagnostic device for the motor of the present application it is possible to detect a change in the operating condition peculiar to the inverter-driven motor separately from the deterioration sign of the motor, and the detection accuracy can be improved. Further, by detecting the installation status of the motor as a variation in the spectral value of the rotation frequency of the motor, it is possible to prevent erroneous detection of the motor diagnosis.
  • FIG. It is a schematic block diagram which shows the installation state of the diagnostic apparatus of the electric motor which concerns on Embodiment 1.
  • FIG. It is a block diagram which shows the structure of the arithmetic processing part in the diagnostic apparatus of the electric motor which concerns on Embodiment 1.
  • FIG. It is a flow diagram explaining the operation of the diagnostic apparatus of the electric motor which concerns on Embodiment 1.
  • FIG. It is a flow diagram explaining the correction method of the diagnostic apparatus of the electric motor which concerns on Embodiment 1.
  • FIG. It is a figure which shows an example of the FFT result correction matrix used for the diagnostic apparatus of the electric motor which concerns on Embodiment 1.
  • FIG. 1 is a schematic configuration diagram showing an installation state of a diagnostic device for an electric motor according to the first embodiment.
  • the main circuit 1 drawn from the power system is provided with a wiring breaker 2, an electromagnetic contactor 3, and a voltage / current detector 4 for detecting the load current of the main circuit 1.
  • a three-phase induction motor is connected to the main circuit 1 as a load motor 5, and the mechanical equipment 6 is driven by the motor 5.
  • the electric motor 5 is an inverter-driven electric motor driven by an inverter.
  • the diagnostic device 100 of the electric motor uses the measuring circuit 7 for inputting the current and the voltage detected by the voltage-current detector 4 and the current input from the measuring circuit 7 to load the electric motor 5 and the mechanical equipment 6 and the like.
  • the arithmetic processing unit 8 for detecting the presence or absence of an abnormality is provided.
  • the rating information setting circuit 9 and the rating information setting circuit for inputting the power supply frequency of the motor 5 and the rated output, the rated current, the number of poles, the rated rotation speed, etc. of the motor 5 into the diagnostic device 100 of the motor in advance.
  • a setting information storage circuit 10 for storing the rating information input from 9 is provided.
  • the rating information is information that can be easily obtained by looking at the catalog of the manufacturer of the motor 5 or the name plate attached to the motor 5. When there are a plurality of electric motors 5 to be diagnosed, it is necessary to input the rating information of the electric motors 5 to be diagnosed in advance, but in the following description, one electric motor 5 will be described.
  • the display unit 11 is connected to the arithmetic processing unit 8, and displays, for example, a physical quantity of the detected load current and an abnormal state and an alarm when the arithmetic processing unit 8 detects an abnormality in the electric motor 5.
  • the drive circuit 12 is connected to the arithmetic processing unit 8 and outputs a control signal for opening and closing the electromagnetic contactor 3 based on the result of calculation by the arithmetic processing unit 8 based on the current signal detected by the voltage / current detector 4. do.
  • the output circuit unit 13 outputs signals such as an abnormal state and a warning from the arithmetic processing unit 8 to the outside.
  • the external monitoring device 200 is composed of, for example, a PC (personal computer) and is connected to the diagnostic device 100 of one or a plurality of electric motors, and appropriately receives the information of the arithmetic processing unit 8 via the communication circuit 14.
  • the operating status of the diagnostic device 100 of the electric motor is monitored.
  • the connection between the external monitoring device 200 and the communication circuit 14 of the diagnostic device 100 may be a cable or a wireless connection.
  • a network may be constructed between the diagnostic devices 100 of a plurality of electric motors and the connection may be made via the Internet.
  • the configuration of the arithmetic processing unit 8 will be described with reference to FIG.
  • the arithmetic processing unit 8 has a load factor calculation unit 110 that calculates the load factor from the current and voltage of the main circuit 1 input from the measurement circuit 7, and a sampling frequency calculation that measures the power supply frequency from the current or voltage and calculates the sampling frequency.
  • Peak detection calculation unit 113 that selects the peak value location of the power spectrum analyzed by the FFT (Fast Frequency) analysis unit 112 and the FFT analysis unit 112 that perform power spectrum analysis using the current of unit 111 and the measurement circuit 7. It is provided with a rotation frequency band detection unit 114 for obtaining a peak portion caused by the rotation frequency of the electric motor 5 from the peak portion detected by the peak detection calculation unit 113.
  • the rotation frequency spectrum value detection unit 115 that extracts the spectrum value of the rotation frequency band detection unit 114
  • the frequency axis conversion calculation unit 119 that matches the frequency of the rotation frequency band of the power spectrum for a plurality of times
  • the frequency axis was converted by the rotation frequency spectrum value moving average buffer 120 stored as a stored value for averaging and arithmetic processing and the rotation frequency band detection unit 114 stored in the rotation frequency spectrum value moving average buffer 120.
  • An averaging calculation unit 121 that performs averaging processing of power spectra for a plurality of times, and a rotation frequency ⁇ value calculation unit 122 that calculates a variation ⁇ of the rotation frequency spectrum value using the stored value of the rotation frequency spectrum value moving average buffer 120.
  • a threshold calculation unit 123 for selecting a threshold for abnormality diagnosis according to the calculation result of the rotation frequency ⁇ value calculation unit 122 is provided.
  • the averaging calculation unit 121 uses the power spectrum averaged by the averaging calculation unit 121 to extract whether or not there are peak points on both sides of the power supply frequency other than the rotation frequency band of the electric motor 5 (hereinafter, the peak points are referred to as sideband waves).
  • the FFT result correction matrix selection unit 118 Selected by the FFT result correction matrix selection unit 118 for determining the correction value of the FFT analysis result from the side band wave extraction unit 116, the rotation frequency band detection unit 114, and the load factor calculation unit 110, and the FFT result correction matrix selection unit 118.
  • the FFT analysis result correction unit 125 that corrects the current FFT analysis result of the electric motor at the time of diagnosis using the correction value, and the correction value data storage unit that stores the correction value information of the FFT analysis result from the two viewpoints of load factor and frequency. It is equipped with 126.
  • the normal state storage unit 124 that stores and stores the measured value of the normal state and the value stored in the normal state storage unit 124 and the current value are compared to make a good / bad judgment diagnosis of the motor.
  • the abnormal state comparison unit 117 for carrying out the above is provided.
  • the sampling frequency calculation unit 111 determines the sampling frequency, and the FFT analysis unit 112 frequency-analyzes the current of the motor 5.
  • the peak detection calculation unit 113 detects the peak portion of the power spectrum analyzed by the FFT analysis unit 112, and the rotation frequency band detection unit 114 obtains the peak portion due to the rotation frequency of the electric motor 5 from the peak portion of the power spectrum.
  • the rotation frequency spectrum value detection unit 115 calculates the spectrum value of the peak portion due to the rotation frequency of the electric motor 5, and the rotation frequency spectrum value moving average buffer 120 calculates the spectrum value of the rotation frequency spectrum value detection unit 115 a plurality of times. Perform the mobile averaging process.
  • the averaging calculation unit 121 performs averaging processing on the power spectra for a plurality of times, and the rotation frequency ⁇ value calculation unit 122 calculates the variation in the spectrum value of the rotation frequency in the rotation frequency spectrum value moving average buffer 120. Further, the spectral value is corrected for each of the operating load factor and the operating frequency of the electric motor 5, and the threshold value for abnormality determination is determined by the threshold value calculation unit 123 based on the variation in the spectral value of the electric motor 5.
  • the threshold calculation unit 123 calculates a threshold value for determining an abnormality of the motor 5 based on the calculation result of the rotation frequency ⁇ value calculation unit 122, and the normal state storage unit 124 uses the calculation result of the averaging calculation unit 121 as the normal state of the motor 5.
  • the load factor calculation unit 110 calculates the operating load factor of the electric motor 5, and the FFT analysis result correction unit 125 corrects the FFT analysis result by the FFT analysis unit 112 at the time of performing the diagnosis.
  • the correction value data storage unit 126 stores the correction value according to the operating load factor, and the FFT result correction matrix selection unit 118 corrects the FFT analysis result according to the operating frequency and the operating load factor value of the motor 5. It is configured to select a value.
  • the abnormal state comparison unit 117 determines the operating status of the motor 5 based on the correction value in the FFT result correction matrix selection unit 118 and the threshold value for determining the abnormality of the motor 5, and in the display unit 11, the abnormal state comparison unit 117 is abnormal. If there is, the abnormal status is displayed.
  • FIG. 3A shows the flow of processing operations in the phase of learning the initial state
  • FIG. 3B shows the flow of processing operations in the phase of performing diagnosis.
  • the feature of this diagnosis is that the initial state of the motor is learned as a normal state, and the relative evaluation is made based on the difference from the current value in the diagnosis.
  • the start of motor operation is detected (step S101), and the current and voltage of the main circuit 1 of the motor 5 are measured by current / voltage measurement. (Step S102).
  • the load factor is calculated from the measured current and voltage values (step S103).
  • Frequency analysis is performed on the main circuit current by FFT analysis (step S104).
  • the rotational vibration intensity is calculated by extracting the rotational signal intensity from the analysis result of the FFT (step S105), and the averaging process for each frequency / load factor is performed (step S106).
  • the averaged value is accumulated a plurality of times for a certain period of time, and initial learning is performed as a definite value in a normal state (step S107).
  • the correction value and the judgment threshold value of the abnormal vibration for stabilizing the diagnosis are calculated by calculating the correction value and the judgment value for each load factor (step S108). Finally, the correction value and the determination threshold value are stored in the correction matrix (step S109).
  • step S110 if the operation of the motor is confirmed (step S110), the current / voltage measurement step S111, the load factor calculation step S112, and the FFT analysis are the same as in the initial learning flow shown in FIG. 3A.
  • the processing up to step S113, rotational vibration intensity extraction step S114, and averaging processing step S115 for each frequency / load factor is executed.
  • a correction matrix position for reading the correction value at the current load factor from the correction matrix learned in the initial learning flow is performed (step S116), and the correction value and the determination threshold value are read.
  • step S118 the initial / current value comparison for comparing the initial learning value and the corrected measured value is executed (step S118), and the abnormality determination is performed based on this (step S119).
  • the correction value and the determination threshold value acquired in the initial calculation can be corrected by referring to the determination threshold value / initial value in step S117, and an accurate determination can be realized.
  • the correction value / judgment threshold calculation step S108 and the correction matrix storage step S109 are the load factor calculated in the load factor calculation step S103 and the motor obtained in the rotation signal intensity extraction step S105.
  • the rotation signal spectrum values used in the diagnosis are averaged based on the rotation frequency. Even if the operating status of the motor fluctuates, it is possible to make a diagnosis without erroneous detection. Further, in the correction value / judgment threshold value calculation step S108 on the initial calculation start flow, the likelihood up to the diagnosis threshold value can be ensured due to factors such as the installation conditions of the motor, and erroneous detection can be prevented.
  • Step S106 for calculating the average for each frequency and load factor in FIG. 3A is carried out, the data is accumulated in the rotation frequency spectrum value moving average buffer 120, and the initial learning step S107 is carried out, and the rotation frequency spectrum value moving average buffer 120 is performed.
  • the average value is calculated from, and as shown in FIG. 4, the unbiased variance is calculated in the flow FL1, then the judgment threshold of the electric motor for the unbiased variance is selected from the judgment threshold selection table in the flow FL2, and the correction value / judgment threshold calculation step.
  • S108 is carried out, and storage in the correction matrix in the correction value data storage unit 126 is carried out (step S109).
  • the correction matrix is a matrix composed of a frequency and a load factor as shown in FIG. 5, and has a cell for storing the determination threshold value selected in the flow of FIG. 4 for each frequency in addition to the correction value.
  • Diagnosis can be performed regardless of the operating status (load fluctuation / frequency fluctuation) of the inverter-driven motor.
  • the diagnostic threshold can be automatically tuned for each frequency of the motor, preventing erroneous detection of diagnosis. It will be possible to detect with high accuracy.
  • the arithmetic processing unit 8 is composed of a processor and a storage device as a hardware configuration.
  • the storage device includes, for example, a volatile storage device for random access memory and a non-volatile auxiliary storage device for flash memory. Further, the auxiliary storage device of the hard disk may be provided instead of the flash memory.
  • the processor executes the program input from the storage device. In this case, the program is input from the auxiliary storage device to the processor via the volatile storage device. Further, the processor may, for example, output the data of the calculation result to the volatile storage device of the storage device, or may store the data in the auxiliary storage device via the volatile storage device.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)
  • Control Of Electric Motors In General (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
PCT/JP2020/042570 2020-11-16 2020-11-16 電動機の診断装置 Ceased WO2022102113A1 (ja)

Priority Applications (5)

Application Number Priority Date Filing Date Title
JP2022561236A JP7422896B2 (ja) 2020-11-16 2020-11-16 電動機の診断装置
KR1020237014758A KR102914035B1 (ko) 2020-11-16 2020-11-16 전동기의 진단 장치
CN202080107078.7A CN116490760B (zh) 2020-11-16 2020-11-16 电动机的诊断装置
DE112020007771.3T DE112020007771T5 (de) 2020-11-16 2020-11-16 Elektromotordiagnoseeinrichtung
PCT/JP2020/042570 WO2022102113A1 (ja) 2020-11-16 2020-11-16 電動機の診断装置

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PCT/JP2020/042570 WO2022102113A1 (ja) 2020-11-16 2020-11-16 電動機の診断装置

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KR (1) KR102914035B1 (https=)
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WO (1) WO2022102113A1 (https=)

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JPWO2025017843A1 (https=) * 2023-07-18 2025-01-23
WO2025105499A1 (ja) * 2023-11-17 2025-05-22 ダイキン工業株式会社 劣化情報システム及びプログラム
WO2025224993A1 (ja) * 2024-04-26 2025-10-30 三菱電機株式会社 電動機の診断装置、および電動機の診断システム
JP7814646B1 (ja) * 2025-06-27 2026-02-16 三菱電機株式会社 電動機負荷設備の診断装置

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JPWO2025017843A1 (https=) * 2023-07-18 2025-01-23
WO2025017843A1 (ja) * 2023-07-18 2025-01-23 三菱電機株式会社 異常判別装置、学習装置、電源装置、異常判別システムおよび異常判別方法
JP7805532B2 (ja) 2023-07-18 2026-01-23 三菱電機株式会社 異常判別装置、電源装置、異常判別システムおよび異常判別方法
WO2025105499A1 (ja) * 2023-11-17 2025-05-22 ダイキン工業株式会社 劣化情報システム及びプログラム
JP2025082846A (ja) * 2023-11-17 2025-05-29 ダイキン工業株式会社 劣化情報システム及びプログラム
JP7755202B2 (ja) 2023-11-17 2025-10-16 ダイキン工業株式会社 劣化情報システム及びプログラム
WO2025224993A1 (ja) * 2024-04-26 2025-10-30 三菱電機株式会社 電動機の診断装置、および電動機の診断システム
JP7814646B1 (ja) * 2025-06-27 2026-02-16 三菱電機株式会社 電動機負荷設備の診断装置

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