Diagnostic device for electric motors

Abstract

In the diagnosis device of the electric motor of the present application, a threshold value calculation section (123) calculates a threshold value for abnormality determination of the electric motor (5) based on the operation result of the rotation frequency σ value operation section (122), a normal state storage section (124) stores the operation result of the averaging operation section (121) as a normal state of the electric motor (5), a load rate calculation section (110) calculates the operation load rate of the electric motor (5), an FFT analysis result correction section (125) corrects the FFT analysis result of the FFT analysis section (112) at the time of implementing the diagnosis, an FFT result correction matrix selection section (118) selects the correction value of the FFT analysis result according to the values of the operation frequency and the operation load rate of the electric motor (5), an abnormal state comparison section (117) determines the operation state of the electric motor (5) based on the correction value of the FFT result correction matrix selection section (118) and the threshold value for abnormality determination of the electric motor (5), and if there is an abnormality, the display is performed on the display section (11).

Description

Technical Field

[0001] This application relates to a diagnostic device for diagnosing whether an inverter-driven motor has any abnormalities. Background Technology

[0002] Against the backdrop of environmental issues in recent years, the use of inverter-driven motors (hereinafter referred to as inverter-driven motors) has been on the rise in order to achieve high-efficiency operation of electric motors. Inverter-driven motors are used in production line equipment in the manufacturing industry and as power sources for machinery. For example, they are widely used in pumps, compressors, fans, and industrial robots, and demand for them is increasing.

[0003] Therefore, such motors are required to operate reliably and continuously at all times. However, not all motors operate in the appropriate operating environment; it is not uncommon for them to operate under high stress environments such as high temperature, high humidity, heavy load, corrosion, and wear.

[0004] Previously, the diagnosis of such equipment was mostly done through time-based maintenance (TBM), with maintenance departments making judgments using sensory diagnostics. Particularly critical motors require regular diagnostics to check for faults, which presents a significant cost issue.

[0005] Therefore, people are paying increasing attention to condition-based maintenance (CBM) technology for electric motors.

[0006] Currently, the diagnosis of motors driven by inverters is achieved by installing various sensors and other measuring devices on each motor. These measuring devices include torque meters, speed and acceleration vibration sensors, etc.

[0007] In addition, Patent Document 1 discloses a device that compares the power spectral density pattern obtained by performing Fourier analysis on various signals representing the state of rotating equipment such as pumps and motors with a reference pattern of various signals under normal conditions, and determines whether there is an abnormality based on the distance between the reference pattern and the reference pattern.

[0008] Existing technical documents

[0009] Patent documents

[0010] Patent Document 1: Japanese Patent Application Publication No. 58-100734 Summary of the Invention

[0011] The technical problem that the invention aims to solve

[0012] However, it is impractical to apply this to motor control centers that centrally manage hundreds or thousands of motors. Therefore, there is a need for a device that can diagnose the motor status of the inverter drive mode based on information such as current and voltage that can be measured through the motor control center, without the need for additional special sensors, thus ensuring reliability, productivity, and safety.

[0013] The speed or operating load of an inverter-driven motor changes constantly with the operator's input. As the speed or load changes, the required diagnostic parameters, such as current or voltage values, also change, making it difficult to determine whether the motor's condition is due to operational factors or deterioration / faults. Therefore, a diagnostic method independent of changes in operating conditions is needed for inverter-driven motors.

[0014] In the abnormality detection device for rotating equipment disclosed in Patent Document 1, considering the operation in the actual environment, it is possible to misdetect changes in the operating conditions specific to the inverter drive mode as motor deterioration. Furthermore, in Patent Document 1, by comparing the threshold used for diagnosis with a pre-given value, depending on the installation condition of the motor, false detection may occur due to deviations in the spectral value of the motor's rotation frequency.

[0015] This application was made to solve the above-mentioned problems. Its purpose is to obtain a diagnostic device for an electric motor that can detect changes in the operating conditions unique to the inverter drive mode separately from the signs of motor deterioration, thereby improving the detection accuracy. In addition, by detecting the deviation of the motor's installation condition as the spectral value of the motor's rotation frequency, false detections in motor diagnosis can be prevented.

[0016] Technical solutions to solve technical problems

[0017] The diagnostic device for an electric motor disclosed in this application includes: a measurement circuit for inputting the current and voltage of an inverter-driven motor; a sampling frequency calculation unit for determining the sampling frequency when the current is in a stable state; an FFT analysis unit for performing frequency analysis on the motor current when the current is in a stable state; a peak detection calculation unit for detecting the peak portion of the power spectrum analyzed by the FFT analysis unit; a rotating frequency band detection unit for determining the peak portion caused by the motor's rotation frequency from the peak portion of the power spectrum; a rotating frequency spectrum value detection unit for calculating the spectrum value of the peak portion caused by the motor's rotation frequency; a rotating frequency spectrum value moving average buffer for performing multiple moving average processing on the spectrum value of the rotating frequency spectrum value detection unit; an averaging calculation unit for averaging the power spectrum multiple times; and a rotating frequency σ value calculation unit. The rotating frequency σ-value calculation unit calculates the deviation of the spectral value of the rotating frequency in the rotating frequency spectral value moving average buffer; the threshold calculation unit calculates a threshold for determining motor abnormalities based on the calculation result of the rotating frequency σ-value calculation unit; the normal state storage unit stores the calculation result of the averaging calculation unit as the normal state of the motor; the load rate calculation unit calculates the operating load rate of the motor; the FFT analysis result correction unit corrects the FFT analysis result of the FFT analysis unit when performing diagnosis; the correction value data storage unit stores the correction value corresponding to the operating load rate; the FFT result correction matrix selection unit selects the correction value of the FFT analysis result based on the values ​​of the motor's operating frequency and operating load rate; and the abnormal state comparison unit determines the operating status of the motor based on the correction value of the FFT result correction matrix selection unit and the threshold for determining motor abnormalities.

[0018] Invention Effects

[0019] According to the diagnostic device for electric motors of this application, it is possible to separate the changes in the unique operating conditions of an inverter-driven electric motor from the signs of motor deterioration, thereby improving the detection accuracy.

[0020] In addition, by detecting the deviation of the motor's rotational frequency spectrum value as a measure of the motor's installation condition, false detections in motor diagnosis can be prevented. Attached Figure Description

[0021] Figure 1 This is a simplified structural diagram showing the installation state of the diagnostic device for the electric motor in Embodiment 1.

[0022] Figure 2 This is a block diagram showing the structure of the arithmetic processing unit in the diagnostic device for the electric motor according to Embodiment 1.

[0023] Figure 3 This is a flowchart illustrating the operation of the diagnostic device for the electric motor in Embodiment 1.

[0024] Figure 4 This is a flowchart illustrating the calibration method of the diagnostic device for the electric motor in Embodiment 1.

[0025] Figure 5 This is a diagram showing an example of the FFT result correction matrix used in the diagnostic device for the electric motor in Embodiment 1. Detailed Implementation

[0026] Hereinafter, a diagnostic device for an electric motor according to an embodiment will be described with reference to the accompanying drawings. Furthermore, in each figure, the same reference numerals denote the same or equivalent parts.

[0027] Implementation method 1.

[0028] Figure 1 This is a simplified structural diagram showing the installation state of the diagnostic device for the electric motor in Embodiment 1.

[0029] In the diagram, the main circuit 1, which is supplied from the power system, includes a wiring circuit breaker 2, an electromagnetic contactor 3, and a voltage and current detector 4 for detecting the load current of the main circuit 1. The main circuit 1 is connected to a motor 5, such as a three-phase induction motor, which serves as the load and drives the mechanical equipment 6. The motor 5 is an inverter-driven motor.

[0030] The diagnostic device 100 for electric motors includes a measurement circuit 7 that receives current and voltage detected by a voltage and current detector 4, and an arithmetic processing unit 8 that uses the current input from the measurement circuit 7 to detect whether there are any abnormalities in the loads of electric motors 5 and mechanical equipment 6.

[0031] Furthermore, the diagnostic device 100 for the electric motor includes: a rated information setting circuit 9, which pre-inputs the power supply frequency, rated output, rated current, number of poles, rated speed, etc. of the electric motor 5; and a setting information storage circuit 10, which stores the rated information input from the rated information setting circuit 9. The rated information is easily obtained by consulting the manufacturer's catalog or the nameplate mounted on the electric motor 5. When multiple electric motors 5 are being diagnosed, the rated information of each electric motor 5 must be pre-input; however, the following description will focus on one electric motor 5.

[0032] 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 displays abnormal status and alarms when the arithmetic processing unit 8 detects an abnormality in the motor 5.

[0033] 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 calculated by the arithmetic processing unit 8 according to the current signal detected by the voltage and current detector 4.

[0034] The output circuit section 13 outputs signals such as abnormal status and warnings from the arithmetic processing section 8 to the outside.

[0035] The external monitoring device 200, such as a PC (personal computer), is connected to one or more motor diagnostic devices 100. It receives information from the processing unit 8 via the communication circuit 14 and monitors the operation of the motor diagnostic devices 100. The connection between the external monitoring device 200 and the communication circuit 14 of the diagnostic device 100 can be via cable or wirelessly. Alternatively, a network can be established between multiple motor diagnostic devices 100 and connected via the Internet.

[0036] use Figure 2 The structure of the arithmetic processing unit 8 will be described. The arithmetic processing unit 8 includes: a load rate calculation unit 110, which calculates the load rate based on the current and voltage of the main circuit 1 input from the measurement circuit 7; a sampling frequency calculation unit 111, which measures the power supply frequency based on the current or voltage and calculates the sampling frequency; an FFT (Fast Fourier Transform) analysis unit 112, which performs power spectrum analysis using the current of the measurement circuit 7; a peak detection calculation unit 113, which selects the peak portion of the power spectrum analyzed by the FFT analysis unit 112; and a rotating frequency band detection unit 114, which determines the peak portion caused by the rotation frequency of the motor 5 from the peak portion detected by the peak detection calculation unit 113.

[0037] In addition, it includes: a rotating frequency spectrum value detection unit 115, which extracts the spectrum value of the rotating frequency band detection unit 114; a frequency axis transformation calculation unit 119, which makes the frequency of the rotating frequency band of multiple power spectra consistent; a rotating frequency spectrum value moving average buffer 120, which saves the spectrum value of the rotating frequency as a storage value for averaging and calculation processing; an averaging calculation unit 121, which performs averaging processing on multiple power spectra obtained by the rotating frequency band detection unit 114 after frequency axis transformation, stored in the rotating frequency spectrum value moving average buffer 120; a rotating frequency σ value calculation unit 122, which uses the storage value of the rotating frequency spectrum value moving average buffer 120 to calculate the deviation σ of the rotating frequency spectrum value; and a threshold calculation unit 123, which selects the threshold for anomaly diagnosis based on the calculation result in the rotating frequency σ value calculation unit 122.

[0038] It also includes: a sideband wave extraction unit 116, which uses the power spectrum averaged by the averaging calculation unit 121 to extract whether there are peak parts (hereinafter, the peak parts are referred to as sideband waves) on both sides of the power frequency other than the rotation frequency band of the motor 5; an FFT result correction matrix selection unit 118, which determines the correction value of the FFT analysis result based on the rotation frequency band detection unit 114 and the load rate calculation unit 110; an FFT analysis result correction unit 125, which uses the correction value selected by the FFT result correction matrix selection unit 118 to correct the motor current FFT analysis result during diagnosis; and a correction value data storage unit 126, which stores the correction value information of the FFT analysis result from the perspectives of load rate and frequency.

[0039] It also includes: a normal state storage unit 124, which saves and stores the measured value of the normal state to compare the abnormal state with the normal state; and an abnormal state comparison unit 117, which performs a good motor condition determination and diagnosis by comparing the value stored in the normal state storage unit 124 with the current value.

[0040] In this embodiment, when the current of the motor 5 is in a stable state, the sampling frequency is determined by the sampling frequency calculation unit 111, and the frequency analysis of the current of the motor 5 is performed by the FFT analysis unit 112.

[0041] 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 determines the peak portion caused by the rotation frequency of the motor 5 from the peak portion of the power spectrum.

[0042] Next, the rotation frequency spectrum value detection unit 115 calculates the spectrum value of the peak part generated by the rotation frequency of the motor 5, and the rotation frequency spectrum value moving average buffer 120 performs multiple moving average processing on the spectrum value of the rotation frequency spectrum value detection unit 115.

[0043] The averaging calculation unit 121 performs averaging processing on the power spectrum multiple times, and the rotating frequency σ value calculation unit 122 calculates the deviation of the rotating frequency spectrum value in the rotating frequency spectrum value moving average buffer 120.

[0044] In addition, the spectrum value is corrected for each operating load rate and operating frequency of the motor 5, and the threshold for anomaly determination is determined by the threshold calculation unit 123 based on the deviation of the spectrum value of the motor 5.

[0045] The threshold calculation unit 123 calculates the threshold for determining the abnormality of the motor 5 based on the calculation result of the rotation frequency σ value calculation unit 122, and the normal state storage unit 124 stores the calculation result of the averaging calculation unit 121 as the normal state of the motor 5.

[0046] The load rate calculation unit 110 calculates the operating load rate of the motor 5, and the FFT analysis result correction unit 125 corrects the FFT analysis result of the FFT analysis unit 112 when performing diagnosis.

[0047] The correction value data storage unit 126 stores the correction value corresponding to the operating load rate, and the FFT result correction matrix selection unit 118 is configured to select the correction value of the FFT analysis result based on the operating frequency and operating load rate of the motor 5.

[0048] 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 for determining the abnormality of the motor 5. If the abnormal state comparison unit 117 determines that there is an abnormality, the display unit 11 displays the abnormal status.

[0049] Next, based on Figure 3 The operation of the motor diagnostic device in Implementation Method 1 will be explained. The motor diagnostics can be divided into two phases: the phase of learning the initial state (normal state) of the motor and the phase of performing the diagnostics. Figure 3 (a) represents the flow of processing actions in the initial learning stage. Figure 3 (b) Describes the process flow of actions during the diagnostic phase. This diagnostic method is characterized by learning the initial state of the motor as a normal state and evaluating it relative to the current value during diagnostic implementation.

[0050] First, as the initial calculation process, such as Figure 3As shown in the flowchart (a), the motor operation is detected to begin (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, the load rate is calculated based on 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 based on the FFT analysis results by extracting the rotational signal intensity (step S105), and averaging is performed on each frequency and load rate (step S106). The averaged values ​​are stored multiple times for a certain period of time and used as the determination value of the normal state for initial learning (step S107). After the initial learning is completed, a stabilization correction value for trend diagnosis and a judgment threshold for abnormal vibration are calculated for each load rate by calculating correction values ​​and judgment values ​​(step S108). Finally, the correction matrix containing the correction values ​​and judgment thresholds is stored (step S109).

[0051] The following section explains the diagnostic initiation process implemented after the learning phase. For example... Figure 3 As shown in (b), if the operation of the motor is confirmed (step S110), then... Figure 3 Similarly, the initial learning process shown in (a) executes the following steps up to the current and voltage measurement step S111, the load rate calculation step S112, the FFT analysis step S113, the rotational vibration intensity extraction step S114, and the averaging process for each frequency and load rate step S115. In the diagnostic process, the position of the correction matrix for the current load rate is read from the correction matrix learned in the initial learning process (step S116), and the correction value and the judgment threshold are read. Then, the process becomes an initial and current value comparison (step S118) for comparing the initial learned value and the corrected measurement value, and an anomaly judgment is performed based on this (step S119).

[0052] exist Figure 3 In the initial calculation process of (a), by averaging each frequency and load rate in step S106 and calculating the correction value and judgment threshold in step S108, false detections due to the operation or running condition of the motor can be avoided. Furthermore, in Figure 3 In the diagnostic start process of (b), by referring to the judgment threshold and the initial value in step S117, the correction value and judgment threshold obtained in the initial calculation are corrected, thereby achieving accurate judgment.

[0053] for Figure 3(a) The initial calculation start process, the correction value and decision threshold calculation step S108, and the correction matrix storage step S109 average the rotational signal spectrum values ​​used in the diagnosis based on the load rate calculated in the load rate calculation step S103 and the motor rotation frequency obtained in the rotational signal intensity extraction step S105. Diagnosis can be performed without false detection even if the motor's operating conditions change. Furthermore, the correction value and decision threshold calculation step S108 in the initial calculation start process ensures the likelihood of the diagnostic threshold among factors such as motor setting conditions, thereby preventing false detection.

[0054] Implementation for computation Figure 3 (a) Step S106, averaging each frequency and load rate, and storing it in the rotating frequency spectrum value moving average buffer 120, performs the initial learning step S107, calculating the average value from the rotating frequency spectrum value moving average buffer 120, such as... Figure 4 As shown, after calculating the unbiased variance in process FL1, in process FL2, a decision threshold for the motor with unbiased variance is selected from the decision threshold selection table, and the correction value and decision threshold calculation step S108 is performed, and the correction matrix is ​​stored in the correction value data storage unit 126 (step S109). Figure 5 As shown, the correction matrix is ​​a matrix composed of frequency and load rate, and in addition to the correction value, it also stores the values ​​for each frequency. Figure 4 The unit selected for the determination threshold in the process.

[0055] Diagnosis can be performed without considering the motor's operating conditions (load variations and frequency variations) based on the inverter drive mode. Furthermore, since the motor's operating speed, a characteristic of the inverter drive mode, changes, the vibration of the motor being diagnosed changes. Therefore, the diagnostic threshold can be automatically tuned according to each frequency of the motor, preventing false detections and enabling high-precision detection.

[0056] Furthermore, the processing unit 8, as a hardware structure, consists of a processor and a storage device. The storage device includes, for example, volatile storage devices such as random access memory (RAM) and non-volatile auxiliary storage devices such as flash memory. Alternatively, an auxiliary storage device such as a hard disk can be used instead of flash memory. The processor executes a 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. Additionally, the processor can, for example, output the result of the calculation to the volatile storage device of the storage device, or save the data to the auxiliary storage device via the volatile storage device.

[0057] This application describes exemplary embodiments, but the various features, methods and functions described in the embodiments are not limited to the application of specific embodiments, and can be applied to the embodiments alone or in various combinations.

[0058] Therefore, it can be assumed that numerous variations not illustrated are also included within the scope of the technology disclosed in this application. For example, this includes cases involving modifications, additions, or omissions of at least one structural element.

[0059] Label Explanation

[0060] 4 Voltage and current detector, 5 Motor, 7 Measurement circuit, 8 Processing unit, 100 Diagnostic device, 110 Load rate calculation unit, 111 Sampling frequency calculation unit, 112 FFT analysis unit, 113 Peak detection calculation unit, 114 Rotating frequency band detection unit, 115 Rotating frequency spectrum value detection unit, 117 Abnormal state comparison unit, 118 FFT result correction matrix selection unit, 120 Rotating frequency spectrum value moving average buffer, 121 Averaging calculation unit, 122 Rotating frequency σ value calculation unit, 123 Threshold calculation unit, 124 Normal state storage unit, 125 FFT analysis result correction unit, 126 Correction value data storage unit.

Claims

1. A motor diagnostic device including an arithmetic processing section that diagnoses an abnormality of a motor driven by an inverter based on a current and a voltage of the motor; the motor diagnostic device characterized by, The arithmetic processing section includes: a measurement circuit for inputting the current and the voltage of the motor; a sampling frequency calculation section for determining a sampling frequency when the current is in a steady state; an FFT analysis section for performing frequency analysis on the current of the motor when the current is in the steady state; a peak detection arithmetic section for detecting a peak portion of a power spectrum analyzed by the FFT analysis section; a rotational frequency band detection section for finding a peak portion due to a rotational frequency of the motor from the peak portion of the power spectrum; a rotational frequency spectrum value detection section for calculating a spectrum value of the peak portion due to the rotational frequency of the motor; a rotational frequency spectrum value moving average buffer for performing a moving average process a plurality of times on the spectrum value of the rotational frequency spectrum value detection section; an averaging arithmetic section that averages the power spectrum a plurality of times; a rotational frequency σ value arithmetic section for performing an arithmetic operation on a deviation of the spectrum value of the rotational frequency in the rotational frequency spectrum value moving average buffer; a threshold value calculation section that calculates a threshold value for performing an abnormality determination of the motor based on an arithmetic result of the rotational frequency σ value arithmetic section; a normal state storage section that stores an arithmetic result of the averaging arithmetic section as a normal state of the motor; a load rate calculation section that calculates an operating load rate of the motor; an FFT analysis result correction section that corrects an FFT analysis result of the FFT analysis section at the time of performing the diagnosis; a correction value data storage section that stores a correction value corresponding to the operating load rate; an FFT result correction matrix selection section that selects a correction value of an FFT analysis result according to a value of an operating frequency and an operating load rate of the motor; and an abnormal state comparison section that determines an operating condition of the motor based on the correction value of the FFT result correction matrix selection section and a threshold value for performing the abnormality determination of the motor.

2. The motor diagnostic device according to claim 1, wherein, the spectrum value is corrected for each operating load rate and operating frequency of the motor.

3. The motor diagnostic device according to claim 1, wherein, a threshold value for the abnormality determination is determined in the threshold value calculation section according to a deviation of the spectrum value of the motor. ​

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