Anomaly detection device, motor system and anomaly detection method

Abstract

To provide a method for determining abnormality of a motor, the method suppressing erroneous detection in the determination of the abnormality of the motor.SOLUTION: An abnormality detection device detecting abnormality of a motor is provided, comprising an acquisition unit that acquires characteristic values related to the motor, and a processing unit that processes the characteristic values acquired by the acquisition unit, and the processing unit performs the steps of: (a) acquiring the characteristic values related to the motor as test data; (b) updating statistics of the test data, (c) updating a fluctuation range from the updated statistics; (d) updating an exponential moving average of the test data; (e) updating an anomaly level on the basis of the test data, the fluctuation range, the statistics, and the exponential moving average; and (f) detecting the abnormality of the motor on the basis of the abnormality level.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to an anomaly detection device, a motor system, and an anomaly detection method. [Background technology]

[0002] Patent Document 1 discloses an abnormality determination device that determines mechanical abnormalities in a motor drive mechanism. Patent Document 1 discloses that the abnormality determination device includes a data abnormality determination unit that determines data abnormalities in time-series data, and a mechanical abnormality determination unit that determines mechanical abnormalities in the motor drive mechanism based on the acquisition mode of the time-series data that has been determined to be abnormal. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-151598 A Summary of the Invention [Problem to be solved by the invention]

[0004] General anomaly detection algorithms detect fluctuations in data. Therefore, for example, if the rotation speed of a variable speed motor is intentionally changed, it may falsely detect an anomaly. Also, if there are gradual changes in the environment in which the motor is installed, it may falsely detect an anomaly.

[0005] The present disclosure provides an anomaly detection device that suppresses erroneous detection in determining an anomaly in a motor. [Means for solving the problem]

[0006] In one aspect of the present disclosure, there is provided an anomaly detection device that detects an anomaly in a motor, the anomaly detection device comprising: an acquisition unit that acquires characteristic values ​​related to the motor; and a processing unit that processes the characteristic values ​​acquired by the acquisition unit, wherein the processing unit executes the following steps: (a) acquiring the characteristic values ​​related to the motor as test data; (b) updating statistics of the test data; (c) updating a fluctuation range from the updated statistics; (d) updating an exponential moving average of the test data; (e) updating an anomaly level based on the test data, the fluctuation range, the statistics, and the exponential moving average; and (f) detecting an anomaly in the motor based on the anomaly level. Effect of the Invention

[0007] According to the motor abnormality detection device of the present disclosure, erroneous detection in determining an abnormality in a motor can be suppressed. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an outline of a motor system in which a motor control device according to this embodiment is used. [Diagram 2] FIG. 2 is a flow diagram illustrating the processing in the motor control device according to this embodiment. [Diagram 3] FIG. 3 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 4] FIG. 4 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Diagram 5] FIG. 5 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 6] FIG. 6 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 7] FIG. 7 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 8] FIG. 8 is a diagram illustrating the operation results of the motor control device of the reference example. [Figure 9] FIG. 9 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 10] FIG. 10 is a diagram illustrating the operation result of the motor control device according to the present embodiment. [Figure 11] FIG. 11 is a diagram illustrating the operation results of the motor control device of the reference example. [Figure 12] FIG. 12 is a diagram illustrating the operation results of the motor control device of the reference example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] <Motor system> A motor system in which a motor control device according to this embodiment is used will be described below with reference to the drawings.

[0010] 1 is a diagram showing an outline of a motor system 1 that uses a motor control device 100, which is an example of a motor control device according to the present embodiment. The motor control device 100 is an example of an abnormality detection device that detects an abnormality in a motor.

[0011] The motor system 1 includes a motor 10 and a motor drive control system 20. The motor 10 is used to rotate a fan, for example. The motor drive control system 20 drives and controls the motor 10.

[0012] The motor drive control system 20 includes a motor control device 100 and a higher-level device 200.

[0013] <Motor control device 100> The motor control device 100 includes a motor drive circuit 101, a sensor unit 102, and an FG (Frequency Generator) signal generation unit 103. The motor control device 100 also includes a power supply voltage measurement unit 110, a drive control signal generation unit 120, a communication unit 130, a current measurement unit 140, a rotational speed measurement unit 150, an abnormality detection control unit 160, and a data management unit 170.

[0014] The drive control signal generating unit 120, the rotation speed measuring unit 150, the abnormality detection control unit 160, and the data management unit 170 in the motor control device 100 are realized, for example, by a program processing device (computer). More specifically, the program processing device includes a processor such as a CPU (Central Processing Unit) and various storage devices such as a RAM (Random Access Memory) and a ROM (Read Only Memory). The program processing device also includes peripheral circuits such as a counter (timer), an AD (Analog-to-digital) conversion circuit, a DA (Digital-to-analog) conversion circuit, a clock generating circuit, and an input / output I / F (Interface) circuit. For example, the processor, the storage device, and the peripheral circuits are connected to each other via a bus or a dedicated line. The program processing device is, for example, a microcontroller. In the program processing device, the CPU executes various arithmetic operations according to the programs stored in the memory to realize the processes.

[0015] [Motor drive circuit 101] The motor drive circuit 101 drives the motor 10 based on a drive control signal Ctl generated by a drive control signal generation unit 120. The motor drive circuit 101 includes, for example, a pre-drive circuit and an inverter circuit.

[0016] The pre-drive circuit generates an output signal for driving the inverter circuit based on the drive control signal Ctl. The pre-drive circuit outputs the generated output signal to the inverter circuit. For example, the pre-drive circuit generates and outputs a drive signal for driving each switch element of the inverter circuit based on the drive control signal Ctl.

[0017] The inverter circuit outputs a drive signal to the motor 10 based on the output signal output from the pre-drive circuit. The drive signal output by the inverter circuit is applied to a coil included in the motor 10. In the inverter circuit, for example, a pair of two switch elements, for example, a series circuit of transistors such as field effect transistors, provided at both ends of a DC power source are connected to the coil of each phase. In each pair of the two switch elements, a terminal of each phase of the motor 10 is connected to a connection point between the switch elements.

[0018] A drive signal generated by the pre-drive circuit turns on and off each switch element constituting the inverter circuit, whereby power is supplied to each phase of the motor 10 and the rotor of the motor 10 rotates.

[0019] The motor drive circuit 101 is supplied with power from a power supply 210 included in the upper device 200. The motor drive circuit 101 is connected to the power supply 210 included in the upper device 200. The motor drive circuit 101 converts the power supplied from the power supply 210 based on a drive control signal Ctl. Then, the motor drive circuit 101 supplies the converted power to the motor 10.

[0020] [Sensor unit 102] The sensor unit 102 detects the rotational position of the rotor of the motor 10. The sensor unit 102 includes, for example, a position sensor. The sensor unit 102 includes, for example, a Hall element. The Hall element of the sensor unit 102 detects the magnetic poles of the rotor. The Hall element of the sensor unit 102 outputs a Hall signal whose voltage changes in response to the rotation of the rotor.

[0021] The position sensor of the sensor unit 102 is not limited to a Hall element. The position sensor may be any sensor capable of detecting the rotational position of the rotor of the motor. For example, an encoder or the like may be used as the position sensor. When an encoder is used as the position sensor, the sensor unit 102 may be provided outside the motor control device 100 as a device independent of the motor control device 100, rather than as one of the components of the motor control device 100.

[0022] [FG signal generation unit 103] The FG signal generating unit 103 generates an FG signal as a rotation speed signal indicating the rotation speed of the motor 10. The FG signal generating unit 103 generates a signal (FG signal) having a period (frequency) proportional to the rotation speed of the motor 10, for example, based on a detection signal (Hall signal) output from a Hall element of the sensor unit 102. The FG signal output from the FG signal generating unit 103 is input to the higher-level device 200. The FG signal generating unit 103 may be realized by, for example, an FG pattern formed on a board (printed board) on which the motor 10 is mounted.

[0023] [Power supply voltage measuring unit 110] The power supply voltage measuring unit 110 measures the voltage V of the power supplied from the higher-level device 200. In other words, the power supply voltage measuring unit 110 measures the power supply voltage. The power supply voltage measuring unit 110 outputs the measured voltage value Vm to the abnormality detection control unit 160.

[0024] [Drive control signal generating unit 120] The drive control signal generating unit 120 generates a drive control signal Ctl for controlling the drive of the motor 10. The drive control signal generating unit 120 receives, for example, a speed command signal Sv which is a drive command signal output as a drive command from the higher-level device 200. When the drive control signal generating unit 120 receives the speed command signal Sv, it generates a drive control signal Ctl so that the rotation speed of the motor 10 coincides with a target rotation speed specified by the speed command signal Sv.

[0025] The drive control signal Ctl is, for example, a PWM (Pulse Width Modulation) signal.

[0026] The drive control signal generating unit 120 includes a speed command analyzing unit 121 , a duty ratio determining unit 122 , and an energization control unit 123 .

[0027] (Speed ​​command analysis unit 121) The speed command analysis unit 121 receives the speed command signal Sv output from the higher-level device 200. Then, the speed command analysis unit 121 analyzes the target rotation speed specified by the speed command signal Sv. For example, when the speed command signal Sv is a PWM signal having a duty ratio corresponding to the target rotation speed, the speed command analysis unit 121 analyzes the duty ratio of the speed command signal Sv and outputs information on the rotation speed corresponding to the duty ratio as the target rotation speed.

[0028] (Duty ratio determination unit 122) The duty ratio determination unit 122 determines the duty ratio of the PWM signal serving as the drive control signal Ctl based on the target rotation speed output from the speed command analysis unit 121 and the measurement value of the rotation speed of the motor 10 measured by the rotation speed measurement unit 150.

[0029] Specifically, the duty ratio determination unit 122 calculates a control value for the motor 10 so as to reduce the difference between the target rotation speed and the measured value of the rotation speed of the motor 10. Then, the duty ratio determination unit 122 determines the duty ratio of the PWM signal according to the calculated control value. For example, the duty ratio determination unit 122 calculates the control value by PID (Proportional-Integral-Differential) control so as to reduce the difference between the target rotation speed and the measured value of the rotation speed of the motor 10. Note that the duty ratio determination unit 122 may calculate the control value by either PD (Proportional-Differential) control or PI (Proportional-Integral) control. Then, the duty ratio determination unit 122 determines the duty ratio Dty of the PWM signal according to the control value.

[0030] The duty ratio determining unit 122 outputs the determined duty ratio Dty to the abnormality detection control unit 160.

[0031] (Electrical supply control unit 123) The energization control unit 123 generates a PWM signal having the duty ratio determined by the duty ratio determination unit 122. Then, the energization control unit 123 outputs the generated PWM signal to the motor drive circuit 101 as a drive control signal Ctl.

[0032] [Communications Department 130] The communication unit 130 communicates with the outside. The communication unit 130 communicates with the higher-level device 200. Specifically, the communication unit 130 transmits and receives data to and from the higher-level device 200 as a control device. The communication unit 130 includes a transmission unit 131, a reception unit 132, and a communication control unit 133.

[0033] The transmitting unit 131 transmits data to the higher-level device 200. The receiving unit 132 receives data from the higher-level device 200. The transmitting unit 131 and the receiving unit 132 are each controlled by a communication control unit 133. The transmitting unit 131 is, for example, a serial communication interface circuit that generates a predetermined serial signal and transmits it to a communication line. The receiving unit 132 is, for example, a serial communication interface circuit that receives a predetermined serial signal from the communication line.

[0034] The communication control unit 133 controls each of the transmitting unit 131 and the receiving unit 132. The communication control unit 133 sends encoded data to the transmitting unit 131. The communication control unit 133 also decodes data received from the receiving unit 132. The communication control unit 133 controls the transmitting unit 131 and the receiving unit 132 to transmit and receive data to and from the higher-level device 200. The communication control unit 133 is realized, for example, by program processing by a processor provided in the motor control device 100.

[0035] The communication control unit 133 receives the speed command signal Sv output as a drive command from the higher-level device 200. Then, the communication control unit 133 transmits the received speed command signal Sv to the speed command analysis unit 121.

[0036] [Current measuring unit 140] The current measurement unit 140 measures the current of the power supplied from the motor drive circuit 101 to the motor 10. The current measurement unit 140 includes, for example, a current transformer. The current measurement unit 140 outputs a current value Im obtained by measuring the current of the power supplied from the motor drive circuit 101 to the motor 10 to the abnormality detection control unit 160.

[0037] [Rotational speed measurement unit 150] The rotation speed measurement unit 150 measures the rotation speed of the motor 10. The rotation speed measurement unit 150 measures the rotation speed of the motor 10 based on, for example, a detection signal (Hall signal) of a Hall element in the sensor unit 102. The rotation speed measurement unit 150 outputs the measured rotation speed Rm to the abnormality detection control unit 160.

[0038] [Abnormality detection control unit 160] The abnormality detection control unit 160 detects an abnormality in the motor 10. The abnormality detection control unit 160 estimates and detects the occurrence of an abnormality in the motor 10 based on any one of the voltage value Vm, the current value Im, the rotation speed Rm of the motor 10, and the duty ratio Dty.

[0039] [Data Management Department 170] The data management unit 170 manages the data.

[0040] <Host device 200> Next, a description will be given of the host device 200. The host device 200 supplies power to the motor control device 100. The host device 200 also instructs the motor control device 100 on the rotation speed. The host device 200 includes a power source 210, a data processing control unit 220, and a communication unit 230.

[0041] [Power supply 210] The power supply 210 supplies the motor control device 100 with power for operating the motor 10. The power supply 210 is a DC power supply. The power supply 210 supplies the motor control device 100 with power of, for example, 12 V (volts).

[0042] [Data processing control unit 220] The data processing control unit 220 instructs the motor control device 100 of a target rotation speed (target rotation speed) of the motor 10. The data processing control unit 220 transmits a speed command signal Sv to the motor control device 100 via the communication unit 230. The data processing control unit 220 instructs the motor control device 100 of a target rotation speed (target rotation speed) by transmitting the speed command signal Sv to the motor control device 100.

[0043] [Communications Department 230] The communication unit 230 communicates with the motor control device 100. The communication unit 230 communicates with the communication unit 130 in the motor control device 100. The communication unit 230 receives a signal transmitted from the transmission unit 131 in the communication unit 130 as a reception signal Rx. The communication unit 230 also transmits the transmission signal Tx to the reception unit 132 in the communication unit 130. The communication unit 230 is, for example, a serial communication interface circuit that generates a predetermined serial signal and transmits and receives the serial signal from a communication line.

[0044] <Processing in the abnormality detection control unit> Next, the processing performed by the abnormality detection control unit 160 will be described. By describing the processing performed by the abnormality detection control unit 160, steps included in a motor abnormality detection method for detecting an abnormality in the motor 10 performed by the motor control device 100 will be described. Also, the procedure of a program executed by a computer included in the motor control device 100 will be described. Figure 2 is a flow diagram illustrating the processing in the motor control device 100, which is an example of a motor control device according to this embodiment.

[0045] The inventors discovered that in order to detect abnormalities in the motor 10, false detections can be suppressed by calculating the standard deviation as a statistical quantity for characteristic values ​​related to the motor 10 during operation and then using the standard deviation to calculate the amplitude of fluctuation.

[0046] The abnormality detection control unit 160 in the motor control device 100 executes an inspection data acquisition step (step S10), a statistics calculation step (step S20), and a fluctuation width calculation step (step S30). The abnormality detection control unit 160 then executes a moving average calculation step (step S40), an abnormality degree calculation step (step S50), and an abnormality determination step (step S60). The abnormality detection control unit 160 repeatedly executes steps S10 to S60 while the motor 10 is operating.

[0047] (Step S10) The abnormality detection control unit 160 acquires sampled characteristic values ​​of the motor 10 as test data while the motor 10 is in operation (test data acquisition step). The characteristic values ​​of the motor 10 include, for example, any one of the current value, voltage value, and rotation speed and duty ratio of the power supplied to the motor 10.

[0048] Here, the n-th test data acquired by the abnormality detection control unit 160 is designated as test data xi(n), where n is an integer equal to or greater than 1.

[0049] (Step S20) Next, the abnormality detection control unit 160 calculates statistics using the acquired test data (statistics calculation step). The abnormality detection control unit 160 calculates the standard deviation of the test data as an example of statistics. To calculate the standard deviation, the abnormality detection control unit 160 calculates the average of the test data and the average of the values ​​obtained by squaring the test data (root mean square).

[0050] A specific calculation method will be described. The abnormality detection control unit 160 updates and calculates the nth average Xa(n) using the nth test data xi(n) and the (n-1)th average Xa(n-1) based on Equation 1. Here, N is an integer of 2 or more. N is, for example, any integer from 100 to 1000.

[0051]

number

[0052] Furthermore, the abnormality detection control unit 160 calculates the nth root mean square Xsa(n) based on Equation 2, using the nth test data xi(n) and the (n-1)th root mean square Xsa(n-1).

[0053]

number

[0054] The root mean square Xsa(n) corresponds to the average of the squared values ​​of the inspection data xi, as shown in Equation 3.

[0055]

number

[0056] Then, the abnormality detection control unit 160 calculates the nth standard deviation σi(n) based on Equation 4, using the nth average Xa(n) and the root mean square Xsa(n).

[0057]

number

[0058] When calculating standard deviation using variance for multiple data, the data used for the calculation must be temporarily stored. As the amount of data increases, the area required to store the data increases, resulting in increased memory consumption.

[0059] Therefore, the anomaly detection control unit 160 calculates the average and the square mean to obtain the standard deviation by using the recurrence formula (exponential moving average) shown in Equation 1 and Equation 2. By using the recurrence formula (exponential moving average), the standard deviation can be calculated by sequential calculation, thereby reducing memory consumption.

[0060] (Step S30) Next, the abnormality detection control unit 160 calculates the fluctuation width Ri at the time of an abnormality based on the statistics calculated in step S20 (fluctuation width calculation step). Specifically, the abnormality detection control unit 160 updates and calculates the nth fluctuation width Ri(n) based on Equation 5 using the nth standard deviation σi(n).

[0061]

number

[0062] The anomaly detection control unit 160 calculates the exponential moving average of the test data. The exponential moving average at the nth time is set as the exponential moving average yi(n). The exponential moving average yi(n) at the nth time is calculated based on Equation 6 using the smoothing coefficient α, the nth test data xi(n), and the exponential moving average yi(n-1) at the n-1th time. Here, α is a constant between 0 and 1.

[0063]

number

[0064] The exponential moving average yi(n) is calculated by the sum of the smoothing coefficient α multiplied by the inspection data xi(n) and the value obtained by subtracting the smoothing coefficient α from 1 and multiplying the exponential moving average yi(n-1). In other words, the exponential moving average yi(n) is updated to a value calculated by the sum of the smoothing coefficient α multiplied by the inspection data xi(n) and the value obtained by subtracting the smoothing coefficient α from 1 and multiplying the exponential moving average yi(n-1).

[0065] The abnormality detection control unit 160 may calculate the exponential moving average value yi(n) based on Equation 7 instead of Equation 6. The abnormality detection control unit 160 may calculate the exponential moving average value yi(n) at the nth point based on Equation 7 using a smoothing coefficient N, the nth test data xi(n), and the exponential moving average value yi(n-1) at the n-1th point. In other words, the abnormality detection control unit 160 may calculate the exponential moving average value yi(n) by setting the smoothing coefficient α to 1 / N+1. Here, N is an integer equal to or greater than 2. N is, for example, any integer between 100 and 1000. N may be referred to as a smoothing coefficient.

[0066]

number

[0067] The exponential moving average yi(n) is updated to the sum of the inspection data xi(n) and the exponential moving average yi(n-1) multiplied by the smoothing coefficient N, and then divided by the smoothing coefficient N plus 1.

[0068] (Step S50) Next, the abnormality detection control unit 160 updates the degree of abnormality based on the test data xi(n), the amplitude Ri(n), the standard deviation σi(n) and the exponential moving average value yi(n) (abnormality calculation step). The abnormality detection control unit 160 updates the degree of abnormality based on the test data xi(n) acquired in step S10, the standard deviation σi(n) calculated in step S20, the amplitude Ri(n) calculated in step S30 and the exponential moving average value yi(n) calculated in step S40. The abnormality detection control unit 160 calculates the degree of change Sd(n) based on Equation 8.

[0069]

number

[0070] Then, the abnormality detection control unit 160 calculates the degree of abnormality Se(n) based on Equation 9.

[0071]

number

[0072] Note that max{a,b} is a function that returns the larger of the values ​​of a and b. In other words, max{0,Se(n-1)+Sd(n)} returns 0 if Se(n-1)+Sd(n) is negative or 0, and returns Se(n-1)+Sd(n) if Se(n-1)+Sd(n) is positive.

[0073] (Step S60) Next, the abnormality detection control unit 160 detects an abnormality in the motor 10 based on the abnormality degree Se(n) (an abnormality detection step). When the abnormality degree Se(n) is greater than a predetermined threshold, the abnormality detection control unit 160 detects that an abnormality has occurred in the motor 10.

[0074] (Step S70) The abnormality detection control unit 160 determines whether or not to end the process. If the process is to be ended (YES in step S70), the abnormality detection control unit 160 ends the process. If the process is not to be ended, in other words, if the process is to be continued (NO in step S70), the abnormality detection control unit 160 returns to step S10 and repeats the process.

[0075] <Operation results of the motor control device> Next, the operation results when the motor control device 100, which is an example of the motor control device according to this embodiment, is operated will be described. Figures 3 and 4 are diagrams for explaining the operation results of the motor control device 100, which is an example of the motor control device according to this embodiment. In the operation results shown in Figures 3 and 4, the current value of the power supplied to the motor 10 is used as the characteristic value related to the motor 10.

[0076] FIG. 3 is a diagram showing the current value of the power supplied to the motor 10. The horizontal axis of FIG. 3 indicates the time elapsed from a predetermined time, and the vertical axis indicates the current value. The unit of time is hours (h), and the unit of the current value is an arbitrary unit. FIG. 4 is a diagram showing the calculated degree of abnormality. The horizontal axis of FIG. 4 indicates the time, and the vertical axis indicates the degree of abnormality. The unit of time is hours (h), and the unit of the degree of abnormality is an arbitrary unit.

[0077] The line Lraw1 in Fig. 3 indicates the test data acquired by the anomaly detection control unit 160. The line Lavg1 in Fig. 3 indicates the exponential moving average of the test data processed in the anomaly detection control unit 160. The exponential moving average was calculated using Equation 7. In Fig. 3, N in Equation 7 was set to 100. The line Lse1 in Fig. 4 indicates the degree of anomaly calculated in the anomaly detection control unit 160.

[0078] In each of Figures 3 and 4, the arrow Pv1 indicates the time when the command speed to the motor 10 changed. In each of Figures 3 and 4, the period PRD1 indicates the period during which the inspection data gradually changes. In each of Figures 3 and 4, the arrow Pna1 indicates the time when a problem occurred in the motor.

[0079] [If there is a sudden change] As an example of a case where there is a sudden change, the operation result when motor control device 100 is operated when there is a change in the command speed of the motor will be described.

[0080] As shown in Fig. 3, in the arrow Pv1, it can be seen that the exponential moving average indicated by the line Lavg1 changes following the inspection data indicated by the line Lraw1. Also, as shown in Fig. 4, in the arrow Pv1, the degree of abnormality does not change significantly. On the other hand, in the arrow Pna1, the degree of abnormality increases. Therefore, by performing threshold processing on the degree of abnormality, it is possible to detect abnormalities.

[0081] [When there is a gradual change] The operational results when motor control device 100 is operated in the case where there is a gradual change will be described.

[0082] As shown in FIG. 3, in the period PRD1, the test data gradually changes over time. As shown in FIG. 3, the exponential moving average indicated by the line Lavg1 changes following the test data indicated by the line Lraw1. Also, as shown in FIG. 4, in the range where the test data gradually changes (period PRD1), the degree of abnormality does not change significantly. On the other hand, the degree of abnormality increases at the arrow Pna1. Therefore, an abnormality can be detected by performing threshold processing on the degree of abnormality.

[0083] [Standard deviation of characteristic values] The characteristic values ​​of the motor 10 when the motor control device 100, which is an example of the motor control device according to this embodiment, is operated will be described. Figs. 5 to 7 are diagrams for explaining the operation results of the motor control device 100, which is an example of the motor control device according to this embodiment. Specifically, Fig. 5 is a diagram showing inspection data when the motor control device 100 is operated. Fig. 6 shows the result of calculating the standard deviation of the current value of the power supplied to the motor 10 in a specified section in the inspection data in Fig. 5. Fig. 7 shows the result of calculating the degree of anomaly by the motor control device 100.

[0084] FIG. 5 is a diagram showing the current value of the power supplied to the motor 10. The horizontal axis of FIG. 5 is the time elapsed from a predetermined time, and the vertical axis is the current value. The unit of time is hours (h), and the unit of the current value is an arbitrary unit. Line Lraw2 in FIG. 5 shows the inspection data acquired by the abnormality detection control unit 160. Line Lavg2 in FIG. 5 shows the exponential moving average of the inspection data processed in the abnormality detection control unit 160. The exponential moving average was calculated using Equation 7. In FIG. 5, N in Equation 7 was set to 100.

[0085] At the arrow Pv2 in Fig. 5, the motor 10 is operating normally, but the current fluctuates greatly due to disturbance. In other words, Fig. 5 shows an example in which the standard deviation has permanently changed due to disturbance. Fig. 6 shows the result of calculating the standard deviation in a specified section for the test data in Fig. 5.

[0086] FIG. 6 is a diagram showing the calculated standard deviation. The line Lstd in FIG. 6 shows the calculated standard deviation. The horizontal axis in FIG. 6 is time, and the vertical axis is standard deviation of the current value. The unit of time is hours (h), and the unit of the standard deviation of the current value is an arbitrary unit. As shown in FIG. 6, the standard deviation suddenly increases at the arrow Pv2. On the other hand, in FIG. 6, the average current value is almost constant.

[0087] Figure 7 shows the results of processing by motor control device 100 on the inspection data shown in Figure 5. Line Lse2 in Figure 7 shows the degree of anomaly calculated by anomaly detection control unit 160. The horizontal axis of Figure 7 is time, and the vertical axis is the degree of anomaly. Note that the unit of time is hours (h), and the unit of the degree of anomaly is an arbitrary unit.

[0088] 7, there is no significant change in the degree of abnormality before and after the arrow Pv2, in other words, before and after a change in the standard deviation of the test data. In other words, by dynamically calculating the standard deviation in motor control device 100, erroneous detection of motor abnormalities can be suppressed.

[0089] The results when the standard deviation is fixed will be described. Fig. 8 is a diagram illustrating the operation results of the motor control device of the reference example showing the results when the standard deviation is fixed. The line Lse2z shows the degree of abnormality calculated with the standard deviation set to a fixed value.

[0090] 8, when the standard deviation of the inspection data is set to a fixed value, the degree of abnormality increases at arrow Pv2. Therefore, the motor control device of the reference example may erroneously detect a motor abnormality.

[0091] According to the motor control device of this embodiment, the standard deviation is dynamically changed to calculate the degree of abnormality, so that even if the standard deviation permanently changes due to an external disturbance, erroneous detection in the motor abnormality determination in which the degree of abnormality increases accordingly can be suppressed.

[0092] [About the smoothing coefficient N] The smoothing coefficient N when operating the motor control device 100, which is an example of a motor control device according to this embodiment, is described below. Figs. 9 and 10 are diagrams for explaining the operation results of the motor control device 100, which is an example of a motor control device according to this embodiment. Specifically, Fig. 9 shows the results of calculations when the smoothing coefficient N is set to 100 for test data that fluctuates greatly over a predetermined time period (arrow Pv3). Fig. 10 shows the results of calculations when the smoothing coefficient N is set to 5000 for the test data in Fig. 9 described above.

[0093] 9 and 10 are diagrams showing the calculated exponential moving averages. The horizontal axis of each of Fig. 9 and Fig. 10 is time, and the vertical axis is current value. The unit of time is hour (h), and the unit of current value is an arbitrary unit.

[0094] As shown in FIG. 9, when the smoothing coefficient N is set to 100, the exponential moving average value indicated by the line Lavg3a appropriately tracks the inspection data.

[0095] Furthermore, as shown in FIG. 10, when the smoothing coefficient N was set to 5000, it took a long time for the exponential moving average value indicated by the line Lavg3b to follow the test data.

[0096] From the above results, the smoothing coefficient N is appropriately determined so that an abnormality can be detected according to the degree of change in the characteristic value. For example, the smoothing coefficient N may be determined in the range of 100 to 1000.

[0097] <Summary> According to the motor control device of this embodiment, it is possible to reduce erroneous detections in determining an abnormality in the motor.

[0098] For example, a case where a sudden change occurs will be described as an example of a motor control device according to a reference example with reference to FIG.

[0099] In the motor control device of the reference example, for example, for the test data shown as line Lraw4, the average value of the test data is first calculated as shown as line Lavga4 in Fig. 11. If the average value of the test data is calculated as shown as line Lavga4 in Fig. 11, it is not possible to respond to sudden changes unless the average value of the test data is calculated again as shown as line Lavgb4 at the time shown by arrow Pv4.

[0100] Further, for example, a motor control device according to a reference example will be described with reference to FIG. 12, taking as an example a case where there is a gradual change.

[0101] For example, in the motor control device of the reference example, if the average value of the test data shown as line Lraw5 is first calculated as shown as line Lavga5 in FIG. 12, the deviation between the test data and the average value becomes larger as time passes.

[0102] According to the motor control device of this embodiment, erroneous detection in the determination of an abnormality in the motor can be suppressed regardless of whether the characteristic value changes suddenly or gradually.

[0103] For example, when a current value is used as the characteristic value, the current measuring unit 140 is an example of an acquisition unit that acquires the characteristic value related to the motor, and the abnormality detection control unit 160 is an example of a processing unit that processes the characteristic value acquired by the acquisition unit.

[0104] <Modification> In step S50, the degree of change may be calculated as the degree of change Sd2(n) instead of the degree of change Sd(n), and the degree of anomaly may be calculated as the degree of anomaly Se2(n) instead of the degree of anomaly Se(n). In other words, the abnormality detection control unit 160 may detect a downward deviation of the characteristic value using a cumulative sum method. The abnormality detection control unit 160 calculates the degree of change Sd2(n) based on Equation 10.

[0105]

number

[0106] Then, the abnormality detection control unit 160 calculates the degree of abnormality Se2(n) based on Equation 11.

[0107]

number

[0108] Note that max{a,b} is a function that returns the larger of the values ​​of a and b. In other words, max{0,Se2(n-1)-Sd2(n)} returns 0 if Se2(n-1)-Sd2(n) is negative or 0, and returns Se(n-1)-Sd2(n) if Se2(n-1)-Sd2(n) is positive.

[0109] In the above embodiment, the current value of the current supplied to the motor 10 is used as the input data indicating the operating state of the motor 10, but the input data is not limited to the current value. For example, the voltage value of the power supplied to the motor, the rotation speed of the motor 10, or the duty ratio may be used as the input data.

[0110] Although the motor control device has been described above by way of an embodiment, the present invention is not limited to the above embodiment. Various modifications and improvements, such as combinations or substitutions with part or all of other embodiments, are possible within the scope of the present invention. [Explanation of symbols]

[0111] 1 Motor System 10 Motor 20 Motor drive control system 100 Motor control device 101 Motor drive circuit 102 Sensor section 103 FG signal generation section 110 Power supply voltage measurement section 120 Drive control signal generator 121 Speed ​​command analysis section 122 Duty ratio determination unit 123 Power supply control unit 130 Communications Department 131 Transmitter 132 Receiving section 133 Communication Control Unit 140 Current measurement section 150 Rotational speed measurement unit 160 Abnormality detection control unit 170 Data Management Department 200 Upper device 210 Power supply

Claims

1. An abnormality detection device that detects an abnormality in a motor, an acquisition unit for acquiring a characteristic value related to the motor; A processing unit that processes the characteristic values ​​acquired by the acquisition unit; Equipped with The processing unit includes: (a) acquiring characteristic values ​​relating to the motor as inspection data; (b) updating statistics of the test data; (c) updating the amplitude from the updated statistics; (d) updating an exponential moving average of the test data; (e) updating the degree of abnormality based on the inspection data, the fluctuation range, the statistics, and the exponential moving average value; (f) detecting an abnormality in the motor based on the degree of abnormality; Execute Anomaly detection device.

2. the characteristic value is any one of a current value, a voltage value, a rotation speed, and a duty ratio of the power supplied to the motor; The anomaly detection device according to claim 1 .

3. In the step (e), the statistic is a standard deviation, a difference between the inspection data and the exponential moving average value is subtracted by half the amplitude, the result is multiplied by the amplitude, and the result is divided by the square of the standard deviation to obtain a degree of change; When the value obtained by adding the degree of abnormality to the degree of change is equal to or greater than zero, the value obtained by adding the degree of abnormality to the degree of change is updated as the degree of abnormality; When the sum of the degree of abnormality and the degree of change is less than zero, the degree of abnormality is updated to zero. The abnormality detection device according to claim 1 or 2.

4. In the step (e), the statistic is a standard deviation, a difference between the inspection data and the exponential moving average value is multiplied by the amplitude, and the result is divided by the square of the standard deviation to obtain a degree of change; When the value obtained by subtracting the degree of change from the degree of abnormality is equal to or greater than zero, the value obtained by subtracting the degree of change from the degree of abnormality is updated as the degree of abnormality; If a value obtained by subtracting the degree of change from the degree of abnormality is less than zero, the degree of abnormality is updated to zero. The abnormality detection device according to claim 1 or 2.

5. In the step (d), the exponential moving average value is updated to a value obtained by dividing the sum of the inspection data and a value obtained by multiplying the exponential moving average value by a smoothing coefficient, by a value obtained by adding 1 to the smoothing coefficient; The anomaly detection device according to claim 1 or 2.

6. The smoothing coefficient is equal to or greater than 100 and equal to or less than 1000. The anomaly detection device according to claim 5 .

7. A motor; an abnormality detection device that detects an abnormality in the motor; A motor system comprising: The abnormality detection device includes: an acquisition unit for acquiring a characteristic value related to the motor; A processing unit that processes the characteristic values ​​acquired by the acquisition unit; Equipped with The processing unit includes: (a) acquiring characteristic values ​​relating to the motor as inspection data; (b) updating statistics of the test data; (c) updating the amplitude from the updated statistics; (d) updating an exponential moving average of the test data; (e) updating the degree of abnormality based on the inspection data, the fluctuation range, the statistics, and the exponential moving average value; (f) detecting an abnormality in the motor based on the degree of abnormality; Execute Motor system.

8. An abnormality detection method for detecting an abnormality in a motor, comprising: (a) acquiring characteristic values ​​relating to the motor as inspection data; (b) updating statistics of the inspection data; (c) updating the amplitude from the updated statistics; (d) updating an exponential moving average of the test data; (e) updating the degree of abnormality based on the inspection data, the amplitude, the statistics, and the exponential moving average value; (f) detecting an abnormality in the motor based on the degree of abnormality; Including, Anomaly detection methods.

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