JP7422896B2 - Electric motor diagnostic equipment - Google Patents

Description

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.

BACKGROUND OF THE INVENTION Against the background of recent environmental problems, the use of a method in which an electric motor is driven by an inverter (hereinafter referred to as an inverter drive method) is on the rise in order to achieve high-efficiency operation of electric motors. Inverter-driven electric motors are used to power equipment and machinery on production lines in the process industry. For example, they are used in a wide variety of applications, including pumps, compressors, blowers, and industrial robots, and demand is on the rise.
Therefore, such electric motors are always required to operate in a healthy and continuous manner. However, not all electric motors are operated in an appropriate usage environment, and it is not uncommon for motors to be operated in high stress environments such as high temperature, high humidity, heavy loads, corrosion, and wear.

Conventionally, diagnosis of such equipment is often determined by a maintenance department using five sense diagnosis using Time Based Maintenance (TBM). Particularly important electric motors require periodic diagnosis for the presence or absence of failure points, which poses a significant problem in terms of cost.
Therefore, there is growing interest in condition-based maintenance technology (CBM) for electric motors.
Diagnosis of current inverter-driven motors is achieved by attaching various sensors and other measuring devices to each motor. Measuring instruments include torque meters, speed and acceleration vibration sensors, and the like.

In addition, in Patent Document 1, a power spectrum density pattern obtained by performing Fourier analysis on various signals indicating the status of rotating equipment such as a pump and an electric motor is compared with a reference pattern of various signals in a normal state, and a reference pattern is A method is disclosed in which the presence or absence of an abnormality is determined based on the distance from the object.

Japanese Unexamined Patent Publication No. 58-100734

However, it is not realistic to apply it to a motor control center that centrally controls hundreds or thousands of electric motors, for example. Therefore, without using special sensors that require additional installation, the status of inverter-driven motors can be diagnosed from information such as current and voltage that can be conventionally measured at a motor control center, improving reliability and productivity. , equipment is required to ensure safety.
The rotational speed or operating load of an inverter-driven electric motor fluctuates from moment to moment depending on the driver's operations. Parameters such as current and voltage values required for diagnosis change as the rotational speed or operating load changes, making it easy to determine whether the motor condition is caused by the operating conditions, or by deterioration or failure. It was difficult to judge. Regarding inverter-driven electric motors, there is a need for a method for diagnosing them regardless of changes in operating conditions.

In the abnormality detection device for rotating equipment disclosed in Patent Document 1, when considering operation in a real environment, there is a possibility that a change in the operating condition peculiar to the inverter drive system is erroneously detected as deterioration of the electric motor. Further, in Patent Document 1, the threshold value for diagnosis is compared with a predetermined value, and depending on the installation situation of the electric motor, there is a possibility of erroneous detection due to variations in the spectrum value of the rotational frequency of the electric motor.

The present application was made to solve the above-mentioned problems, and it is possible to detect changes in the operating conditions peculiar to the inverter drive system by separating them from signs of deterioration of the electric motor, thereby improving detection accuracy. It is an object of the present invention to provide a motor diagnostic device that can prevent erroneous detection of motor diagnosis by detecting the installation status of the motor as a variation in the spectrum value of the rotational frequency of the motor.

The motor diagnostic device disclosed in the present application includes a measurement circuit that inputs current and voltage of a motor driven by an inverter, a sampling frequency calculation unit that determines a sampling frequency when the current is in a stable state, and a sampling frequency calculation unit that determines a sampling frequency when the current is in a stable state. an FFT analysis section that performs frequency analysis of the current of the motor when A rotational frequency band detection unit that calculates the location, a rotational frequency spectrum value detection unit that calculates the spectrum value of the peak location due to the rotational frequency of the motor, and multiple moving average processing for the spectrum values of the rotational frequency spectrum value detection unit. A rotating frequency spectrum value moving average buffer for performing the rotation frequency spectrum value moving average buffer, an averaging calculation unit that averages the power spectrum of multiple times, and a rotation frequency spectrum value moving average buffer that calculates the dispersion of the spectrum value of the rotation frequency in the rotation frequency spectrum value moving average buffer. A σ value calculation unit, a threshold calculation unit that calculates a threshold value for determining abnormality of the motor based on the calculation result of the rotational frequency σ value calculation unit, and a normal state memory that stores the calculation result of the averaging calculation unit as the normal state of the motor. a load factor calculation section that calculates the operating load factor of the motor; an FFT analysis result correction section that corrects the FFT analysis result by the FFT analysis section when performing diagnosis; and a correction value that stores a correction value according to the operating load factor. a data storage section, an FFT result correction matrix selection section that selects a correction value of the FFT analysis result according to the values of the operating frequency and operating load factor of the motor, and an FFT result correction matrix selection section that selects the correction value and abnormality determination of the motor in the FFT result correction matrix selection section. The system is equipped with an abnormal state comparison section that determines the operating status of the electric motor based on the threshold value determined.

According to the electric motor diagnostic device of the present application, it becomes possible to detect changes in the specific operating conditions of an inverter-driven electric motor separately from signs of deterioration of the electric motor, thereby improving detection accuracy.
Furthermore, by detecting the installation status of the motor as variations in the spectrum values of the rotational frequency of the motor, it is possible to prevent erroneous detection in motor diagnosis.

1 is a schematic configuration diagram showing an installed state of a motor diagnostic device according to a first embodiment; FIG. FIG. 2 is a block diagram showing the configuration of an arithmetic processing unit in the electric motor diagnostic device according to the first embodiment. FIG. 3 is a flow diagram illustrating the operation of the electric motor diagnostic device according to the first embodiment. FIG. 3 is a flow diagram illustrating a correction method of the electric motor diagnostic device according to the first embodiment. FIG. 3 is a diagram showing an example of an FFT result correction matrix used in the electric motor diagnostic device according to the first embodiment.

Hereinafter, a motor diagnostic device according to an embodiment will be described with reference to the drawings. In each figure, the same reference numerals indicate the same or corresponding parts.

Embodiment 1.
FIG. 1 is a schematic configuration diagram showing an installed state of a motor diagnostic device according to the first embodiment.
In the figure, a main circuit 1 drawn in from the power system is provided with a molded circuit breaker 2, an electromagnetic contactor 3, and a voltage/current detector 4 for detecting the load current of the main circuit 1. For example, a three-phase induction motor is connected to the main circuit 1 as an electric motor 5 serving as a load, and the mechanical equipment 6 is driven by the electric motor 5. The electric motor 5 is an inverter-driven electric motor driven by an inverter.
The electric motor diagnostic device 100 includes a measuring circuit 7 that inputs the current and voltage detected by the voltage/current detector 4, and uses the current input from the measuring circuit 7 to check the load of the electric motor 5, mechanical equipment 6, etc. It includes an arithmetic processing unit 8 that detects the presence or absence of an abnormality.

The motor diagnostic device 100 also includes a rating information setting circuit 9 into which the power frequency of the motor 5, rated output, rated current, number of poles, rated rotation speed, etc. of the motor 5 are input in advance, and a rating information setting circuit. A setting information storage circuit 10 for storing 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 electric motor 5 or the name plate attached to the electric motor 5. Note that if there are multiple electric motors 5 to be diagnosed, it is necessary to input rating information of the electric motors 5 to be diagnosed in advance, but in the following explanation, one electric motor 5 will be explained.

The display unit 11 is connected to the arithmetic processing unit 8 and displays, for example, the 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 section 8 and outputs a control signal for opening and closing the electromagnetic contactor 3 based on the result of the arithmetic processing performed by the arithmetic processing section 8 based on the current signal detected by the voltage/current detector 4. do.
The output circuit section 13 outputs signals such as abnormal conditions and warnings from the arithmetic processing section 8 to the outside.

The external monitoring device 200 is composed of, for example, a PC (personal computer), is connected to one or more electric motor diagnostic devices 100, and receives information from the arithmetic processing unit 8 via the communication circuit 14 as appropriate. The operating status of the electric motor diagnostic device 100 is monitored. The connection between the external monitoring device 200 and the communication circuit 14 of the diagnostic device 100 may be made using a cable or wirelessly. A network may be constructed between the diagnostic apparatuses 100 for a plurality of electric motors, and the connection may be made via the Internet.

The configuration of the arithmetic processing section 8 will be explained using FIG. 2. The arithmetic processing unit 8 includes a load factor calculation unit 110 that calculates the load factor from the current and voltage of the main circuit 1 inputted from the measurement circuit 7, and a sampling frequency calculation unit that measures the power supply frequency from the current or voltage and calculates the sampling frequency. FFT (Fast Fourier Transform) analysis section 112 that performs power spectrum analysis using the current of measurement circuit 7 and peak detection calculation section 113 that selects the peak value location of the power spectrum analyzed by FFT analysis section 112. and a rotational frequency band detection unit 114 that determines a peak location resulting from the rotational frequency of the electric motor 5 from the peak location detected by the peak detection calculation unit 113.

Further, a rotational frequency spectrum value detection unit 115 extracts the spectrum value of the rotational frequency band detection unit 114, a frequency axis conversion calculation unit 119 that adjusts the frequency of the rotational frequency band of the power spectrum for a plurality of times, and a rotational frequency spectral value. The frequency axis is converted by the rotating frequency spectrum value moving average buffer 120 which stores the values as stored values for averaging and calculation processing, and the rotating frequency band detecting unit 114 stored in the rotating frequency spectrum value moving average buffer 120. An averaging calculation unit 121 that averages power spectra of multiple times; and a rotational frequency σ value calculation unit 122 that calculates the variation σ of rotational frequency spectrum values using the values stored in the rotational frequency spectrum value moving average buffer 120. , a threshold calculation unit 123 that selects a threshold for abnormality diagnosis according to the calculation result of the rotational frequency σ value calculation unit 122.

Furthermore, using the power spectrum averaged by the averaging calculation unit 121, it is extracted whether there are peak points on both sides of the power supply frequency other than the rotational frequency band of the electric motor 5 (hereinafter, the peak points are referred to as sideband waves). ) FFT result correction matrix selection unit 118 that determines the correction value of the FFT analysis result from the sideband extraction unit 116, the rotational frequency band detection unit 114, and the load factor calculation unit 110; an FFT analysis result correction unit 125 that corrects the current FFT analysis result of the motor during diagnosis using the correction value obtained from the calculation; and a correction value data storage unit that stores correction value information of the FFT analysis result from two viewpoints of load factor and frequency. 126.

Furthermore, a normal state storage section 124 stores and stores measured values of the normal state in order to compare the abnormal state and the normal state, and a diagnosis is made to determine the quality of the motor by comparing the values stored in the normal state storage section 124 with the current values. The abnormal state comparison unit 117 is provided.

In this embodiment, when the current of the motor 5 is in a stable state, the sampling frequency calculation section 111 determines the sampling frequency, and the FFT analysis section 112 analyzes the frequency of the current of the motor 5.
The peak detection calculation unit 113 detects the peak location of the power spectrum analyzed by the FFT analysis unit 112, and the rotational frequency band detection unit 114 determines the peak location due to the rotational frequency of the electric motor 5 from the peak location of the power spectrum.
Next, the rotational frequency spectrum value detection unit 115 calculates the spectrum value at the peak point due to the rotational frequency of the electric motor 5, and the rotational frequency spectrum value moving average buffer 120 calculates the spectrum value of the rotational frequency spectrum value detection unit 115 multiple times. Perform moving average processing.
The averaging calculation unit 121 averages the power spectra of a plurality of times, and the rotation frequency σ value calculation unit 122 calculates variations in the rotation frequency spectrum values in the rotation frequency spectrum value moving average buffer 120.
In addition, the spectrum value is corrected for each operating load factor and operating frequency of the electric motor 5, and the threshold value for abnormality determination is determined in the threshold value calculation unit 123 based on the dispersion of the spectrum value of the electric motor 5.

The threshold calculation unit 123 calculates a threshold for determining the abnormality of the motor 5 based on the calculation result of the rotational frequency σ 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. be memorized as
The load factor calculation section 110 calculates the operating load factor of the electric motor 5, and the FFT analysis result correction section 125 corrects the FFT analysis result by the FFT analysis section 112 when performing diagnosis.
The correction value data storage section 126 stores correction values according to the operating load factor, and the FFT result correction matrix selection section 118 corrects the FFT analysis results according to the operating frequency of the electric motor 5 and the operating load factor. Configured to select a value.
The abnormal state comparison section 117 determines the operating status of the electric motor 5 based on the correction value in the FFT result correction matrix selection section 118 and the threshold value for determining the abnormality of the electric motor 5. If there is an abnormal condition, it will be displayed.

Next, the operation of the electric motor diagnostic apparatus according to the first embodiment will be explained with reference to FIG. Motor diagnosis can be divided into two phases: a phase in which the initial state (normal state) of the motor is learned, and a phase in which the diagnosis is implemented. FIG. 3(a) shows the flow of processing operations in the phase of learning the initial state, and FIG. 3(b) shows the flow of processing operations in the phase of performing diagnosis. This diagnosis is characterized in that the initial state of the motor is learned as a normal state, and a relative evaluation is performed based on the difference from the current value in the diagnosis.

First, in the initial calculation flow, as shown in the flowchart shown in FIG. 3(a), 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 and voltage measurement. (Step S102). Next, a 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). Rotational vibration intensity is calculated by extracting rotational signal intensity from the FFT analysis result (step S105), and averaging processing is performed for each frequency and load factor (step S106). Initial learning is performed by accumulating the averaged value multiple times for a certain period of time and using it as a determined value of the normal state (step S107). After the initial learning is completed, a correction value for stabilizing diagnosis and a judgment threshold for abnormal vibration are calculated by calculating correction values and judgment values for each load factor (step S108). Finally, the correction values and determination thresholds are stored in a correction matrix (step S109).

Next, a diagnosis start flow performed after learning will be explained. As shown in FIG. 3(b), once the operation of the motor is confirmed (step S110), the current and voltage measurement step S111, load factor calculation step S112, and FFT analysis are performed similarly to the initial learning flow shown in FIG. 3(a). The processes up to step S113, rotational vibration intensity extraction step S114, and averaging process step S115 for each frequency/load factor are executed. In the diagnosis, a correction matrix position is executed to read a correction value at the current load factor from the correction matrix learned in the initial learning flow (step S116), and the correction value and determination threshold value are read. Then, an initial/current value comparison is performed to compare the initial learned value and the corrected measured value (step S118), and an abnormality determination is performed based on this (step S119).

In the initial calculation start flow shown in FIG. 3(a), it is possible to avoid erroneous diagnosis due to motor operation or operating conditions by averaging processing for each frequency and load factor in step S106 and calculating correction values and judgment thresholds in step S108. can. Further, in the diagnosis start flow shown in FIG. 3(b), the correction value and determination threshold obtained in the initial calculation are corrected by referring to the determination threshold and initial value in step S117, thereby making it possible to realize accurate determination.

Regarding the initial calculation start flow in FIG. 3A, the correction value/judgment threshold calculation step S108 and the correction matrix storage step S109 are based on the load factor calculated in the load factor calculation step S103 and the electric motor obtained in the rotation signal strength extraction step S105. The rotation signal spectrum values used in diagnosis are averaged based on the rotation frequency. Even if the operating status of the electric motor changes, it is possible to diagnose it without erroneously detecting it. In addition, the correction value/judgment threshold calculation step S108 in the initial calculation start flow can ensure the likelihood up to the diagnosis threshold due to factors such as the installation conditions of the electric motor, and can prevent false detection.

Step S106 of calculating the average for each frequency and load factor in FIG. As shown in FIG. 4, after calculating the unbiased variance in flow FL1, in flow FL2, the determination threshold of the motor for the unbiased variance is selected from the determination threshold selection table, and the correction value/determination threshold calculation step S108 is executed, and the correction value data is stored in the correction matrix in the correction value data storage unit 126 (step S109). The correction matrix is a matrix consisting of frequencies and load factors as shown in FIG. 5, and has cells that store, for each frequency, determination thresholds selected in the flow of FIG. 4 in addition to correction values.

Diagnosis can be performed regardless of the operating status (load fluctuation, frequency fluctuation) of an inverter-driven electric motor. In addition, since the vibration phenomenon of the motor to be diagnosed changes by manipulating the operating rotational speed of the motor, which is a feature of the inverter drive system, the diagnostic threshold can be automatically tuned for each motor frequency, preventing false detections in diagnosis. It becomes possible to detect with high precision.

Note that the arithmetic processing unit 8 includes a processor and a storage device as a hardware configuration. The storage device includes, for example, a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory. Further, an auxiliary storage device such as a 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 to the processor from the auxiliary storage device via the volatile storage device. Furthermore, the processor may output the data of the calculation result to a volatile storage device of the storage device, or may store the data in an auxiliary storage device via the volatile storage device.

Although this application describes exemplary embodiments, the various features, aspects, and functions described in the embodiments are not limited to the application of particular embodiments, and may be used alone or It is applicable to the embodiments in various combinations.
Accordingly, countless variations not illustrated are envisioned within the scope of the technology disclosed herein. For example, this includes cases in which at least one component is modified, added, or omitted.

4 voltage and current detector, 5 electric motor, 7 measurement circuit, 8 arithmetic processing unit, 100 diagnostic device, 110 load factor calculation unit, 111 sampling frequency calculation unit, 112 FFT analysis unit, 113 peak detection calculation unit, 114 rotation frequency band detection 115 Rotation frequency spectrum value detection unit, 117 Abnormal state comparison unit, 118 FFT result correction matrix selection unit, 120 Rotation frequency spectrum value moving average buffer, 121 Averaging calculation unit, 122 Rotation frequency σ value calculation unit, 123 Threshold calculation section, 124 normal state storage section, 125 FFT analysis result correction section, 126 correction value data storage section

Claims (3)

A motor diagnostic device comprising an arithmetic processing unit that diagnoses an abnormality in the motor based on the current and voltage of the motor driven by an inverter,
The arithmetic processing unit includes a measurement circuit that inputs the current and voltage of the motor;
a sampling frequency calculation unit that determines a sampling frequency when the current is in a stable state;
an FFT analysis unit that performs frequency analysis of the current of the motor when the current is in a stable state;
a peak detection calculation unit that detects a peak location of the power spectrum analyzed by the FFT analysis unit;
a rotational frequency band detection unit that determines a peak location due to the rotational frequency of the electric motor from a peak location of the power spectrum;
a rotational frequency spectrum value detection unit that calculates a spectrum value at a peak point due to the rotational frequency of the electric motor;
a rotation frequency spectrum value moving average buffer for performing a plurality of moving average processing on the spectrum values of the rotation frequency spectrum value detection unit;
an averaging calculation unit that averages the power spectra of multiple times;
a rotational frequency σ value calculation unit that calculates variations in the rotational frequency spectrum value in the rotational frequency spectrum value moving average buffer;
a threshold value calculation unit that calculates a threshold value for determining an abnormality of the electric motor based on the calculation result of the rotational frequency σ value calculation unit;
a normal state storage unit that stores the calculation result of the averaging calculation unit as a normal state of the electric motor;
a load factor calculation unit that calculates an operating load factor of the electric motor;
an FFT analysis result correction unit that corrects the FFT analysis result by the FFT analysis unit when performing diagnosis;
a correction value data storage unit that stores a correction value according to the operating load factor;
an FFT result correction matrix selection unit that selects a correction value of the FFT analysis result according to the operating frequency and operating load factor of the electric motor;
A diagnostic device for an electric motor, comprising: an abnormal state comparison section that determines an operating condition of the electric motor based on a correction value in the FFT result correction matrix selection section and a threshold value for determining an abnormality of the electric motor. The electric motor diagnostic device according to claim 1, wherein the spectrum value is corrected for each operating load factor and operating frequency of the electric motor. The electric motor diagnostic apparatus according to claim 1, wherein the threshold value calculation unit determines a threshold value for abnormality determination based on variations in the spectrum values of the electric motor.

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