JP2023177596A - Diagnosis device and method for diagnosis - Google Patents

Abstract

To provide a technique that can increase the accuracy of a diagnosis about an abnormality of an electric motor.SOLUTION: A diagnosis device 75 according to an embodiment of the present disclosure includes: a filter unit 752 for acquiring data of a physical amount regarding operations of a motor EM; a feature amount acquisition unit 756 for acquiring a feature amount regarding a component of a specific frequency correlated to the rotation frequency of the motor EM in waveform data of the physical amount regarding operations of the motor EM on the basis of the data acquired by the filter unit 752; and a diagnosis unit 758 for diagnosing an abnormality of the motor EM on the basis of the feature amount of plural pieces of data with different rates of rotation or different ranges of rate of rotation of the motor EM acquired by the filter unit 752.SELECTED DRAWING: Figure 9

Description

The present disclosure relates to a diagnostic device and the like.

For example, there are known techniques for diagnosing abnormalities in a motor based on data on physical quantities related to the operation of the motor (eg, motor current) (see Patent Documents 1 to 4).

JP2021-85820 Publication JP2020-114084 Publication JP2021-114895 Publication Patent No. 6818155 Public Relations

Incidentally, signs of abnormality in the electric motor may appear in specific frequency components that are correlated with the rotation frequency of physical quantity data regarding the operation of the electric motor.

However, depending on the rotational frequency (rotational speed) of the motor, there is a possibility that external factors may cause changes in the frequency components of the physical quantity data related to motor operation that are the same as or close to the specific frequency at which signs of motor abnormality appear. There is. As a result, changes caused by external factors in physical quantity data related to the operation of the electric motor may be mistakenly diagnosed as a sign of abnormality in the electric motor, and the accuracy of the diagnosis may be reduced.

Therefore, in view of the above problems, it is an object of the present invention to provide a technique that can improve the accuracy of diagnosis regarding abnormalities in electric motors.

To achieve the above object, in one embodiment of the present disclosure,
a first acquisition unit that acquires data on physical quantities related to the operation of the electric motor;
a second acquisition unit that acquires, based on the data acquired by the first acquisition unit, a feature quantity related to a component of a specific frequency that has a correlation with the rotational frequency of the electric motor in the waveform data of the physical quantity;
a diagnosis unit that diagnoses an abnormality of the electric motor based on the feature amount corresponding to each of a plurality of data in which the rotational speed or rotational speed range of the electric motor is different from each other, acquired by the first acquisition unit; prepare,
A diagnostic device is provided.

Additionally, in other embodiments of the present disclosure,
a first acquisition step of acquiring physical quantity data regarding the operation of the electric motor;
a second acquisition step of acquiring, based on the data acquired in the first acquisition step, a feature quantity related to a component of a specific frequency that has a correlation with the rotational frequency of the electric motor in the waveform data of the physical quantity;
a diagnosis step of diagnosing an abnormality of the electric motor based on the feature amount corresponding to each of a plurality of data in which the rotational speed or rotational speed range of the electric motor differs from each other, acquired in the first acquisition step; include,
A diagnostic method is provided.

According to the embodiments described above, it is possible to improve the accuracy of diagnosis regarding abnormalities in the electric motor.

FIG. 1 is a diagram showing an example of a diagnostic system. FIG. 2 is a functional block diagram showing an example of a functional configuration of a control circuit. It is a functional block diagram showing an example of the functional configuration of a diagnostic device. It is a figure showing an example of a filter part. FIG. 3 is a diagram illustrating an example of a feature amount related to an abnormality of an electric motor. FIG. 7 is a diagram illustrating another example of feature amounts related to an abnormality of an electric motor. FIG. 3 is a diagram showing an example of the relationship between the rotational frequency of an electric motor and a feature amount. FIG. 7 is a diagram showing another example of the relationship between the rotational frequency of the electric motor and the feature amount. It is a flowchart which shows roughly an example of main processing of a diagnostic device. It is a subflow chart which shows roughly an example of acceleration control processing in main processing of a diagnostic device. FIG. 3 is a diagram showing an example of the relationship between a frequency command and an actual rotational frequency (actual frequency) during acceleration of the electric motor. FIG. 7 is a diagram showing another example of the relationship between the frequency command and the actual rotational frequency (actual frequency) during acceleration of the electric motor. It is a subflow chart which shows roughly an example of diagnostic processing in main processing of a diagnostic device.

Embodiments will be described below with reference to the drawings.

[Hardware configuration of diagnostic system]
Referring to FIG. 1, the hardware configuration of a diagnostic system 1 according to this embodiment will be described.

FIG. 1 is a diagram showing an example of a diagnostic system 1 according to the present embodiment.

As shown in FIG. 1, the diagnostic system 1 includes an electric motor EM, a power conversion device 100, a rotational state sensor 150, a management device 200, and a terminal device 300.

The diagnostic system 1 diagnoses abnormalities in the electric motor EM in the power conversion device 100 (diagnostic device 75 described below).

The abnormalities of the electric motor EM to be diagnosed by the diagnostic system 1 include abnormalities of the electric motor EM caused by temporary causes and abnormalities (deterioration abnormalities) caused by cumulative causes of the electric motor EM. Further, the abnormalities to be diagnosed by the diagnostic system 1 include mechanical abnormalities and electrical abnormalities. Specifically, the abnormality of the electric motor EM to be diagnosed by the diagnostic system 1 depends on the rotational frequency of the electric motor EM in the waveform data of the physical quantity related to the operation of the electric motor EM (that is, it has a phase difference with the rotational frequency of the electric motor EM). This is an anomaly whose influence appears on specific frequency components. The physical quantity related to the operation of the electric motor EM is, for example, the electric current of the electric motor EM. For example, the mechanical abnormality of the electric motor EM to be diagnosed by the diagnostic system 1 includes, for example, a bearing abnormality. Further, the electrical abnormality of the electric motor EM to be diagnosed by the diagnostic system 1 includes, for example, insulation deterioration (rare short) of the electric motor EM.

Diagnosis regarding an abnormality in the electric motor EM includes, for example, diagnosing whether or not there is an abnormality in the electric motor EM. Further, the diagnosis regarding the abnormality of the electric motor EM may include a diagnosis of the presence or absence of a sign of abnormality of the electric motor EM. Further, the diagnosis regarding the abnormality of the electric motor EM may include the diagnosis of the degree of abnormality of the electric motor EM.

The electric motor EM drives, for example, production equipment and mechanical equipment installed in a factory. The electric motor EM is, for example, an AC motor such as an induction motor or a synchronous motor.

The power conversion device 100 converts three-phase AC power (for example, R phase, S phase, and T phase) input from a commercial power supply PS into three-phase AC power (for example, U phase, V phase) having a predetermined voltage and a predetermined frequency. phase, and W phase) to drive the electric motor EM.

The power conversion device 100 includes a rectifier circuit 10, a smoothing circuit 20, an inverter circuit 30, a current sensor 40, a voltage sensor 50, a gate drive circuit 60, a control circuit 70, a diagnostic device 75, and a display section 80. and a communication section 90.

The rectifier circuit 10 is configured to be able to rectify three-phase AC power input from a commercial power supply PS and output DC power. The rectifier circuit 10 has positive and negative output terminals connected to one end of the positive line PL and the negative line NL, respectively, and can output DC power to the smoothing circuit 20 through the positive line PL and the negative line NL. . For example, as shown in FIG. 1, the rectifier circuit 10 is a bridge type full-wave rectifier circuit that includes six semiconductor diodes SD, and in which three sets of series-connected bodies of two semiconductor diodes SD forming upper and lower arms are connected in parallel. be. In this case, the R-phase, S-phase, and T-phase input lines are each connected to the midpoint of the three sets of upper and lower arms.

The smoothing circuit 20 suppresses pulsations in the DC power output from the rectifier circuit 10 and the DC power regenerated from the inverter circuit 30 and smooths them.

For example, as shown in FIG. 1, the smoothing circuit 20 includes a smoothing capacitor 21.

The smoothing capacitor 21 may be provided in parallel with the rectifier circuit 10 and the inverter circuit 30 in a path connecting the positive line PL and the negative line NL.

The smoothing capacitor 21 smoothes the DC power output from the rectifier circuit 10 and the DC power output (regenerated) from the inverter circuit 30 while repeating charging and discharging as appropriate.

The number of smoothing capacitors 21 may be one. Further, a plurality of smoothing capacitors 21 may be arranged, and a plurality of smoothing capacitors 21 may be connected in parallel or in series between the positive line PL and the negative line NL. Further, the plurality of smoothing capacitors 21 may be configured such that two or more series-connected smoothing capacitors are connected in parallel between the positive line PL and the negative line NL.

Further, the smoothing circuit 20 may include a reactor.

The reactor may be provided on the positive line PL between the rectifier circuit 10 and the smoothing capacitor 21 (specifically, at the branch point with the path where the smoothing capacitor 21 is arranged).

The reactor smoothes the DC power output from the rectifier circuit 10 and the DC power output (regenerated) from the inverter circuit 30 while appropriately generating voltage to prevent changes in current.

The inverter circuit 30 has its positive and negative input ends connected to the other ends of the positive line PL and the negative line NL. The inverter circuit 30 converts the DC power supplied from the smoothing circuit 20 into three-phase AC power (U phase, V phase, and W phase) having a predetermined frequency and a predetermined voltage by switching the semiconductor switch SW. Output to electric motor EM. The semiconductor switch SW is, for example, an IGBT (Insulated Gate Bipolar Transistor), a MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor), or a HEMT (High Electron Mobility Transistor). The semiconductor switch SW is composed of, for example, silicon (Si) as a main material. Moreover, the semiconductor switch SW may be configured using a wide bandgap semiconductor material as a main material. Examples of wide band gap semiconductor materials include silicon carbide (SiC), gallium nitride (GaN), gallium oxide (Ga ₂ O ₃ ), carbon (diamond: C), and the like.

For example, as shown in FIG. 1, inverter circuit 30 includes six semiconductor switches SW. Specifically, the inverter circuit 30 includes a bridge circuit in which three series-connected bodies (switch legs) of two semiconductor switches SW forming upper and lower arms are connected in parallel between a positive line PL and a negative line NL. good. In this case, the inverter circuit 30 outputs three-phase AC power through three output lines drawn out from the connection points of the three sets of upper and lower arms. Furthermore, freewheeling diodes may be connected in parallel to each of the six semiconductor switches SW.

The current sensor 40 detects the current of each of the three-phase (three) output lines of the power conversion device 100, that is, the current of each of the three phases of the electric motor EM. The current sensor 40 detects current using, for example, a Hall element, a shunt resistor, a magnetoresistive element, a flux gate, etc., and obtains a detected value (digital value) of the current using an AD (Analog-Digital) converter. The current sensor 40 outputs a signal corresponding to the detected value of the current of each of the three phases of the electric motor EM, and the output signal of the current sensor 40 is taken into the control circuit 70.

Note that the current sensor 40 may detect only the current of two arbitrary phases among the three-phase output lines of the power converter 100. In this case, the control circuit 70 may obtain (calculate) the current value of the remaining one phase from the detected values of the two-phase currents. Further, the control circuit 70 acquires the current values of the three-phase output lines of the power converter 100 based on the current values of the DC links (positive line PL and negative line NL) and the switching pattern of the semiconductor switch SW, for example. (calculation). In this case, the control circuit 70 may acquire (calculate) the current value of the DC link based on the output of the voltage sensor 50.

Voltage sensor 50 detects the voltage (DC link voltage) between positive line PL and negative line NL of power converter 100. The voltage sensor 50 outputs a signal corresponding to the voltage value between the positive line PL and the negative line NL, and the output signal of the voltage sensor 50 is taken into the control circuit 70.

Under the control of the control circuit 70, the gate drive circuit 60 outputs a drive signal for switching (ON/OFF) the six semiconductor switches SW of the inverter circuit 30 to the gate terminal of each of the six semiconductor switches SW.

The control circuit 70 performs control regarding the power conversion device 100.

The functions of the control circuit 70 may be realized by arbitrary hardware or a combination of arbitrary hardware and software. The control circuit 70 includes a computer including a CPU (Central Processing Unit), a memory device, an auxiliary storage device, and an interface device, a drive circuit that drives gate terminals of semiconductor switches, and the like. The memory device is, for example, SRAM (Static Random Access Memory). The auxiliary storage device is, for example, an EEPROM (Electrically Erasable Programmable Read-Only Memory) or a flash memory. The interface device includes, for example, an external interface for connecting to an external recording medium, a communication interface for communicating with other devices, and the like. The control circuit 70 can implement various functions by loading programs installed in the auxiliary storage device into the memory device and executing them on the CPU. Further, the control circuit 70 can import and install a program from a recording medium through an external interface, and can import and install a program from another device through a communication interface.

For example, the control circuit 70 controls the inverter circuit 30 so that the electric motor EM operates under predetermined operating conditions, and drives the electric motor EM under the predetermined operating conditions.

Note that the functions of the control circuit 70 may be realized in a distributed manner by a plurality of control circuits installed in the power conversion device 100.

The diagnostic device 75 diagnoses abnormalities in the electric motor EM.

The functions of the diagnostic device 75 may be realized by arbitrary hardware or a combination of arbitrary hardware and software. The diagnostic device 75 is configured by a computer including a CPU, a memory device, an auxiliary storage device, an interface device, and the like. The memory device is, for example, an SRAM. The auxiliary storage device is, for example, an EEPROM or a flash memory. The interface device includes, for example, an external interface for connecting to an external recording medium, a communication interface for communicating with other devices, and the like. The diagnostic device 75 can realize various functions by loading programs installed in the auxiliary storage device into the memory device and executing them on the CPU. Furthermore, the diagnostic device 75 can import and install a program from a recording medium through an external interface, and can import and install a program from another device through a communication interface.

Note that the functions of the diagnostic device 75 may be realized in a distributed manner by a plurality of diagnostic devices installed in the power conversion device 100.

Under the control of the control circuit 70 and the diagnostic device 75, the display unit 80 provides information regarding the power conversion device 100 to users (for example, workers in a factory where production equipment or mechanical equipment driven by the electric motor EM is installed). Display information. The display unit 80 includes, for example, a warning light, an electronic bulletin board, a liquid crystal display, an organic EL (Electroluminescence) display, and the like.

The communication unit 90 communicates with an external device of the power conversion device 100 through a predetermined communication line.

The predetermined communication line may be, for example, a one-to-one communication line. In addition, the predetermined communication line includes, for example, a local network (LAN: Local Area Network) such as a field network built in a facility (factory) where production equipment, mechanical equipment, etc. driven by the electric motor EM are installed. May be included. The local network may be constructed by wire or wirelessly, or may include both. Further, the predetermined communication line may include, for example, a wide area network (WAN) outside a facility (factory) in which production equipment, mechanical equipment, etc. driven by the electric motor EM are installed. The wide area network may include, for example, a mobile communication network that terminates at a base station, a satellite communication network that uses communication satellites, an Internet network, and the like. Further, the predetermined communication line may include, for example, a short-distance communication line based on a predetermined wireless communication standard such as Bluetooth (registered trademark) or WiFi.

Note that the function of the communication unit 90 may be incorporated in the control circuit 70 or the diagnostic device 75 as a function of an interface device.

The rotational state sensor 150 is attached to the electric motor EM, for example, and detects the rotational position and rotational speed of the electric motor EM. For example, the rotation state sensor 150 is an optical or magnetic encoder. The rotational state sensor 150 outputs a signal corresponding to the detected value of the rotational speed of the electric motor EM, and the output signal of the rotational state sensor 150 is taken into the control circuit 70 of the power conversion device 100. Thereby, the control circuit 70 can grasp the magnetic pole position and rotation speed of the rotor of the electric motor EM based on the detection signal of the rotation state sensor 150.

Management device 200 is provided outside power conversion device 100. The management device 200 is communicably connected to the power conversion device 100 as a host device of the power conversion device 100, and manages the power conversion device 100 and the electric motor EM.

For example, the management device 200 acquires data regarding the states of the power conversion device 100 and the electric motor EM from the power conversion device 100, and performs processing related to the monitoring function of the conditions of the power conversion device 100 and the electric motor EM. Furthermore, the management device 200 performs processing related to an interface function related to interaction between the power conversion device 100 and a user such as a worker or manager of a factory where the power conversion device 100 and the electric motor EM are installed, for example. Specifically, the management device 200 may perform processing for providing information regarding the electric motor EM and the power conversion device 100, or receiving input from the user and transmitting it to the power conversion device 100.

The management device 200 is, for example, an edge controller such as a PLC (Programmable Logic Controller) that manages field devices including the power conversion device 100 in a factory or the like where mechanical equipment or production equipment driven by an electric motor EM is installed. Further, the management device 200 is, for example, a terminal device for managing factory machinery, production equipment, and the like. The management terminal device may be, for example, a stationary computer terminal such as a desktop PC (Personal Computer) installed in an office such as a factory. Further, the management terminal device may be a portable terminal device (portable terminal) that can be carried by a factory manager, worker, etc., such as a tablet terminal, a smartphone, or a laptop PC. Furthermore, the management device 200 is, for example, a server device. The server device may be, for example, an on-premises server or a cloud server that is installed remotely in a factory or the like where production equipment or mechanical equipment driven by the electric motor EM is installed. Further, the server device may be an edge server installed in the premises of a factory or the like where production equipment or mechanical equipment electrically driven by the electric motor EM is installed or in a facility in the vicinity thereof.

The functions of the management device 200 are realized by arbitrary hardware or a combination of arbitrary hardware and software. For example, the management device 200 is mainly configured with a computer including a CPU, a memory device, an auxiliary storage device, a high-speed arithmetic device, and an interface device. Furthermore, the management device 200 may include user interface devices such as an input device and a display device. The memory device includes, for example, SRAM, DRAM (Dynamic Random Access Memory), and the like. The auxiliary storage device includes, for example, an HDD (Hard Disc Drive), an SSD (Solid State Drive), an EEPROM, a flash memory, and the like. The high-speed arithmetic device includes, for example, a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), and the like. The interface device includes, for example, an external interface for connecting to an external recording medium, a communication interface for communicating with other devices, and the like. The management device 200 can realize various functions by loading programs installed in the auxiliary storage device into the memory device and executing them on the CPU. Furthermore, the management device 200 can import and install a program from a recording medium through an external interface, and can import and install a program from another device through a communication interface. Input devices include, for example, a keyboard, a mouse, a touch panel, and the like. The display device includes, for example, a liquid crystal display, an organic EL display, and the like.

Terminal device 300 is a user terminal provided outside power conversion device 100 and used by a user of diagnostic system 1. Users of the diagnostic system 1 are, for example, managers and workers of a factory in which production equipment and mechanical equipment driven by the electric motor EM are installed. The terminal device 300 , for example, provides the user with various information regarding the power converter 100 and the electric motor EM, receives various inputs from the user, and transmits the received inputs to the power converter 100 . The terminal device 300 may acquire information regarding the electric motor EM and the power conversion device 100 via the management device 200, or may directly acquire information regarding the electric motor EM and the power conversion device 100 from the power conversion device 100. Similarly, the terminal device 300 may transmit various inputs from the user to the power electronics device 100 via the management device 200, or may directly transmit inputs from the user to the power electronics device 100.

The terminal device 300 may be a stationary terminal device such as a desktop PC, or a portable terminal device (mobile terminal) such as a smartphone, tablet terminal, or laptop PC. It's okay.

The functions of the terminal device 300 are realized by arbitrary hardware or a combination of arbitrary hardware and software. For example, the terminal device 300 is mainly configured with a computer including a CPU, a memory device, an auxiliary storage device, an interface device, and user interface devices such as an input device and a display device. The memory device includes, for example, SRAM, DRAM, and the like. The auxiliary storage device includes, for example, an HDD, SSD, EEPROM, flash memory, and the like. The interface device includes, for example, an external interface for connecting to an external recording medium, a communication interface for communicating with other devices, and the like. The terminal device 300 can realize various functions by loading programs installed in the auxiliary storage device into the memory device and executing them on the CPU. Further, the terminal device 300 can import and install a program from a recording medium through an external interface, and can import and install a program from another device through a communication interface. Input devices include, for example, button switches, keyboards, mice, touch panels, and the like. The display device includes, for example, a liquid crystal display, an organic EL display, and the like.

[Functional configuration of control circuit]
Next, the functional configuration of the control circuit 70 will be described with reference to FIG. 2.

FIG. 2 is a functional block diagram showing an example of the functional configuration of the control circuit 70. As shown in FIG.

As shown in FIG. 2, the control circuit 70 includes a speed adjustment section 701, a current detection section 702, a vector conversion section 703, a current adjustment section 704, a vector inverse conversion section 705, and a voltage compensation section as functional sections. 706 and a gate signal output section 707.

The speed adjustment unit 701 reduces the deviation to zero based on a command value of the rotational speed of the electric motor EM (hereinafter referred to as "speed command value") and a detected value of the rotational speed of the electric motor EM (hereinafter referred to as "speed detection value"). A control command value regarding the current of the electric motor EM (hereinafter referred to as "current command value") is output to bring the current of the electric motor EM closer to the current. The speed command value is designated according to predetermined operating conditions of the electric motor EM. Further, the speed detection value is obtained based on a signal taken in from the rotation state sensor 150. In this example, the speed adjustment unit 701 outputs current commands for the d-axis and q-axis of a dq rotation coordinate system fixed to the electric motor EM. The speed adjustment unit 701 is, for example, a PI (Proportional Integral) controller.

For example, when sensorless control is employed, an estimated value of the rotational speed of the electric motor EM is used instead of the detected speed of the electric motor EM. In this case, rotation state sensor 150 is omitted. Moreover, when speed control is not adopted, the speed adjustment section 701 is omitted. For example, when torque control or current control is employed, the speed adjustment section 701 is omitted. In this case, the current command value is generated based on the torque command value in torque control, or is generated based on the U-phase, V-phase, and W-phase current command values in current control.

The current detection unit 702 obtains and outputs detected current values of the U-phase, V-phase, and W-phase of the electric motor EM based on the signal taken in from the current sensor 40.

The vector conversion unit 703 converts the output of the current detection unit 702 (current detection values of the U phase, V phase, and W phase) into d of the dq rotation coordinate system based on the electric phase angle of the electric motor EM, information on the magnetic pole position, etc. Converts and outputs the detected current values for the axis and q-axis.

Based on the deviation between the d-axis and q-axis current command values and the d-axis and q-axis current detection values, the current adjustment unit 704 adjusts the d-axis and q-axis voltages of the electric motor EM to bring the deviation close to zero. outputs a command value (hereinafter referred to as "voltage command value") related to the voltage. The current regulator 704 is, for example, a PI controller.

The vector inverse conversion unit 705 converts the d-axis and q-axis voltage command values into U-phase, V-phase, and W-phase voltage command values based on the electrical phase angle of the electric motor EM, information on the magnetic pole position, etc. Output.

The voltage compensator 706 adjusts the output of the vector inverse converter 705 (U phase, V phase, and Correct the voltage command value of W phase). For example, the voltage compensator 706 corrects the voltage command value for voltage compensation regarding the dead time of the inverter circuit 30, and outputs the corrected voltage command values of the U phase, V phase, and W phase.

Note that the voltage compensator 706 may be omitted.

The gate signal output unit 707 generates a command signal regarding control of the inverter circuit 30 (hereinafter referred to as “gate signal”) based on the output of the voltage compensator 706 (voltage command values of U phase, V phase, and W phase). and outputs it to the gate drive circuit 60. The gate signal is, for example, a PWM (Pulse Width Modulation) signal. For example, the gate signal output section 707 includes comparators corresponding to each of the U phase, V phase, and W phase, and the comparator compares each of the voltage command values of the U phase, V phase, and W phase with the carrier wave. By doing so, U-phase, V-phase, and W-phase gate signals are output. Thereby, the control circuit 70 can output a gate signal to the gate drive circuit 60 to control the inverter circuit 30.

[Functional configuration of diagnostic device]
Next, the functional configuration of the diagnostic device 75 will be described with reference to FIGS. 3 to 8.

FIG. 3 is a functional block diagram showing an example of the functional configuration of the diagnostic device 75. FIG. 4 is a diagram showing an example of the filter section 752. FIG. 5 is a diagram illustrating an example of feature amounts related to an abnormality in the electric motor EM. FIG. 6 is a diagram illustrating another example of feature amounts related to an abnormality in the electric motor EM. FIG. 7 is a diagram showing an example of the relationship between the rotational frequency of the electric motor EM and the feature amount. FIG. 8 is a diagram showing another example of the relationship between the rotational frequency of the electric motor EM and the feature amount.

As shown in FIG. 3, the diagnostic device 75 includes an acceleration control section 751, a filter section 752, a peak/valley detection section 753, a waveform counting section 754, an amplitude analysis section 755, and a feature amount acquisition section 756 as functional sections. , an external factor extraction section 757 , a diagnosis section 758 , and a notification section 759 .

The acceleration control unit 751 performs control to accelerate the electric motor EM (acceleration control) when acquiring physical quantity data related to the operation of the electric motor EM (for example, current of the electric motor EM), which is used for diagnosis of the electric motor EM. conduct. For example, the acceleration control unit 751 outputs a rotation frequency command (frequency command) for accelerating the electric motor EM to the control circuit 70 while referring to the detected speed value of the electric motor EM input from the control circuit 70. , performs acceleration control of electric motor EM.

The filter unit 752 extracts specific frequency components of the d-axis and q-axis current detection values based on the output of the vector conversion unit 703 (d-axis and q-axis current detection values) input from the control circuit 70. . For example, the filter unit 752 extracts specific frequency components of the d-axis and q-axis current detection values of the electric motor EM when the electric motor EM is accelerating in accordance with the acceleration control of the acceleration control unit 751, and extracts them at regular intervals. Output as time series data. Thereby, the filter unit 752 can acquire a plurality of time-series data for a certain period at different timings when the electric motor EM is accelerating. Hereinafter, for convenience, the time series data of the specific frequency components of the d-axis and q-axis current detection values of the motor EM output from the filter unit 752 will be referred to as "time series data to be analyzed". There are cases. Furthermore, time-series data of a plurality of analysis targets acquired by the filter unit 752 at different timings may be simply referred to as "time-series data of a plurality of analysis targets."

The specific frequency is a frequency related to an abnormality in the electric motor EM. Specifically, as described above, the specific frequency is a frequency at which an abnormal influence appears on the waveform data of the physical quantity related to the operation of the electric motor EM (current of the electric motor EM), and is a frequency that varies depending on the rotational frequency of the electric motor EM. It is. The specific frequency may be defined according to the type of abnormality to be diagnosed, and may be one or multiple.

For example, as shown in FIG. 4, the filter section 752 includes a tracking filter TF.

The tracking filter TF can extract the rotational frequency component of the electric motor EM from the detected current values of the d-axis and the q-axis by the rotational coordinate conversion section TF1 and the low-pass filter section TF2. This is because, on the coordinate system in which the electric motor EM rotates at an angular velocity ω, the rotational frequency components of the d-axis and q-axis current detection values correspond to the DC component. Then, the tracking filter TF can output a specific frequency component that depends on the rotation frequency in the waveform data of the d-axis and q-axis current detection values by the fixed coordinate conversion unit TF3. Therefore, the filter unit 752 can extract data of a specific frequency component that depends on the rotational frequency in the waveform data of the detected current values on the d-axis and q-axis so as to follow the change in the rotational frequency of the electric motor EM. .

The peak-trough detection unit 753 detects the peaks, that is, the maximum values (mountains) and minimum values (troughs) of the time-series data of the specific frequency components of the d-axis and q-axis currents of the motor EM, which are output from the filter unit 752. do. For example, the peak-trough detection unit 753 detects the maximum value and the time-series data of specific frequency components of the d-axis and q-axis current detection values for a certain period, which are output from the filter unit 752 and are subject to analysis. Detect local minimum values. Then, the peak-trough detection unit 753 outputs time-series data of peak values (maximum values and minimum values) corresponding to each of the time-series data to be analyzed.

For example, the peak/trough detection unit 753 monitors specific frequency components of the d-axis and q-axis that are sequentially input, and detects the change point (peak) from rise to fall or the change point (trough) from fall to rise. do.

As a result, the time series data to be analyzed is compressed into data corresponding to its start point, end point, and peak point (peaks and valleys). Therefore, it is possible to reduce the number of times the processing related to the diagnostic function is executed, and to suppress the processing load on the diagnostic device 75.

Note that the peak and valley detection section 753 may be omitted.

The waveform counting unit 754 extracts amplitude values for each of the plurality of waveforms of the time series included in the time series data to be analyzed, and calculates the amplitude values for each of the plurality of time series data to be analyzed. Outputs the amplitude value data corresponding to . The waveform included in the time series data to be analyzed is, for example, a waveform of one period. Further, the waveform included in the waveform data may include a one-cycle waveform and a half-cycle waveform.

For example, the waveform counting section 754 uses a known waveform counting method based on the output of the peak/trough detection section 753 (i.e., time series data of peak values) to calculate a plurality of time series data included in the time series data to be analyzed. Extract the amplitude value for each waveform. The waveform counting method is, for example, the maximum-minimum method. Further, the waveform counting method may be a maximum value minimum value method, an amplitude method, a level crossing method, or a range bear method. Further, the waveform counting method may be a rainflow method.

Based on the output of the waveform counting section 754, the amplitude analysis section 755 performs analysis on the amplitude values of a plurality of waveforms included in the time series data for each of the time series data to be analyzed. For example, the amplitude analysis unit 755 obtains (creates) and outputs a frequency distribution of amplitude values for each of a plurality of waveforms included in the time series data to be analyzed, for each of the time series data to be analyzed.

Specifically, the amplitude analysis unit 755 divides the entire predetermined range of amplitude values into a plurality of small ranges, and totals the frequency of the amplitude values for each of the small ranges, thereby analyzing the time series data to be analyzed. A frequency distribution of amplitude values for each of a plurality of waveforms included in the waveform may be created. The plurality of small ranges may all have the same size, with the entire range divided at equal intervals, or may have at least a portion of different sizes.

Based on the output of the amplitude analysis unit 755 (that is, the frequency distribution of amplitude values for each of the plurality of waveforms included in the time-series data to be analyzed), the feature value acquisition unit 756 calculates, for each of the plurality of time-series data to be analyzed, A feature quantity (hereinafter simply referred to as a "feature quantity") regarding an abnormality of the electric motor EM is acquired.

For example, as shown in FIG. 5, the feature amount is the frequency value of a small range where the amplitude value is relatively large with respect to a predetermined standard (threshold value Ath) among the entire range of amplitude values (see the circle frame in the figure). ) is obtained based on As shown in FIG. 5A, when the motor EM is normal, the amplitude value of the time series data to be analyzed converges to a relatively small range, but as shown in FIG. 5B, when an abnormality occurs in the motor EM, This is because it tends to extend over a relatively large range. The amplitude value being relatively large with respect to the threshold value Ath may mean that the amplitude value is greater than or equal to the threshold value Ath, or may be greater than the threshold value Ath.

For example, the feature amount obtaining unit 756 obtains, as the feature amount, the total value of frequency values in a small range in which the amplitude value is relatively large with respect to the threshold value Ath in the frequency distribution.

Further, the feature amount acquisition unit 756 acquires the feature amount based on the evaluation value regarding the relationship between the frequency of each small range where the amplitude value in the frequency distribution is relatively large with respect to the threshold value Ath and a predetermined standard (threshold value). It's okay. In this case, the threshold value regarding the frequency may be the same for each small range, or may be different from each other as shown in FIG. For example, the feature amount acquisition unit 756 calculates an evaluation value of the degree to which the frequency deviates in a direction larger than the threshold value for each small range in which the amplitude value is relatively large with respect to the threshold value Ath in the frequency distribution. , the sum of the evaluation values is obtained as a feature quantity.

The external factor extraction unit 757 extracts feature quantities that include the influence of external factors from the feature quantities for each of the plurality of analysis target time series data acquired by the feature quantity acquisition unit 756.

For example, as shown in FIG. 7, when an abnormality occurs in the electric motor EM, the feature amount for each time series data of multiple analysis targets corresponding to different rotational speed ranges of the electric motor EM is the rotational frequency during acceleration of the electric motor EM. There is a tendency for values to be relatively large compared to normal values across the entire range. Further, when an abnormality occurs in the electric motor EM, the feature amount for each time series data of a plurality of analysis targets changes in accordance with the change in the rotational frequency of the electric motor EM. As mentioned above, the time-series data to be analyzed is a component of a specific frequency that depends on the rotational frequency of the electric motor EM in the waveform data of physical quantities related to the operation of the electric motor EM, and the manner in which the influence of abnormalities appears also depends on the rotational frequency of the electric motor EM. This is because it depends. In this example (Fig. 7), when the electric motor EM is abnormal, the feature values for each time-series data of multiple analysis targets change linearly in accordance with the increase in the rotational frequency of the electric motor EM. .

On the other hand, as shown in FIG. 8, the influence of a specific external factor occurs only at a fixed frequency fr1 that depends on the external factor. Therefore, only the feature amount when the rotational frequency of the electric motor EM is frequency fr1 or a band close thereto becomes relatively larger than other feature amounts. Therefore, the external factor extraction unit 757 evaluates the magnitude of change with respect to other feature quantities with adjacent rotation frequency ranges and the rate of change with respect to changes in rotation frequency for each feature quantity of time series data to be analyzed. By doing so, it is possible to extract feature quantities that include the influence of external factors.

The diagnosis unit 758 diagnoses the abnormality of the electric motor EM based on the output of the feature amount acquisition unit 756 (that is, the feature amount for each time series data of a plurality of analysis targets).

Specifically, the diagnosis unit 758 diagnoses the abnormality of the electric motor EM based on the feature quantities excluding the feature quantity extracted by the external factor extraction unit 757 among the feature quantities for each time series data of a plurality of analysis targets. I do. Hereinafter, for convenience, the feature quantities for each time-series data of multiple analysis targets, excluding the feature quantities extracted by the external factor extraction unit 757, may be referred to as "feature quantities to be considered." be.

For example, as shown in FIGS. 7 and 8, the diagnosis unit 758 diagnoses an abnormality in the electric motor EM based on the tendency of the feature quantity to be examined with respect to a change in the rotational frequency. Specifically, the diagnostic unit 758 diagnoses that the electric motor EM is abnormal when it can be determined that the feature value to be considered represents a predetermined change in response to a change in the rotational frequency, and detects the predetermined change. If it cannot be determined that this is the case, it may be diagnosed that the electric motor EM is normal. The predetermined change is defined by, for example, the rotational frequency of the electric motor EM, a linear relationship with respect to the square of the rotational frequency, the magnitude of a change rate with respect to a change in the rotational frequency of the electric motor EM, and the like. Furthermore, the diagnostic unit 758 may diagnose the degree of abnormality in the electric motor EM according to the degree of change in the feature amount to be considered with respect to a change in the rotational frequency.

In addition, the diagnosis unit 758 uses a reference pattern (hereinafter referred to as " Based on the comparison with the reference pattern ("reference pattern"), an abnormality in the electric motor EM may be diagnosed. Specifically, the diagnosis unit 758 determines that there is an abnormality in the electric motor EM when the degree of deviation between the change in the feature amount to be considered with respect to the rotational frequency and the reference pattern is relatively large with respect to a predetermined reference. good. Furthermore, the diagnosis unit 758 may diagnose the degree of deviation of the electric motor EM according to the change in the rotational frequency of the feature amount to be considered and the degree of deviation from the reference pattern.

The notification unit 759 notifies the user of the diagnosis result by the diagnosis unit 758.

For example, the notification unit 759 displays information regarding the diagnosis result on the display unit 80. Further, the notification unit 759 may transmit a signal including information regarding the diagnosis result to the management device 200 and the terminal device 300 through the communication unit 90. Thereby, the notification unit 759 can notify the user of the diagnosis result through the management device 200 and the terminal device 300.

Note that information regarding the diagnosis result may be transmitted from the power conversion device 100 (communication unit 90) to the terminal device 300 via the management device 200.

[Series of processes for diagnosing motor abnormalities]
Next, with reference to FIGS. 9 to 13, a series of processes for diagnosing an abnormality in the electric motor will be described.

FIG. 9 is a flowchart schematically showing an example of the main processing of the diagnostic device 75. FIG. 10 is a sub-flowchart schematically showing an example of the acceleration control process in the main process of the diagnostic device 75. FIG. 11 is a diagram showing an example of the relationship between the frequency command and the actual rotational frequency (actual frequency) during acceleration of the electric motor EM. FIG. 12 is a diagram showing another example of the relationship between the frequency command and the actual rotational frequency (actual frequency) during acceleration of the electric motor EM. FIG. 13 is a sub-flowchart schematically showing an example of diagnostic processing in the main processing of the diagnostic device 75.

The flowchart in FIG. 9 is executed, for example, at a predetermined timing for diagnosing an abnormality in the electric motor EM. The predetermined timing is, for example, when the electric motor EM is started. Thereby, it is possible to diagnose an abnormality in the electric motor EM by utilizing the timing of accelerating the electric motor EM from a zero state to a rotational speed range during operation.

As shown in FIG. 9, in step S102, the acceleration control unit 751 performs acceleration control processing to accelerate the electric motor EM. For example, the acceleration control unit 751 moves to the subflowchart of FIG. 10.

As shown in FIG. 10, in step S202, the acceleration control unit 751 starts outputting a frequency command for accelerating the electric motor EM. For example, when the flowchart in FIG. 9 is executed when the electric motor EM is started, the acceleration control unit 751 starts outputting a frequency command to increase the rotational speed of the electric motor EM from a zero state to a speed range during operation. .

Upon completion of the process in step S202, the diagnostic device 75 proceeds to step S204.

In step S204, the acceleration control unit 751 determines whether the frequency command has changed, that is, whether it has actually started outputting the frequency command corresponding to acceleration of the electric motor EM. If the frequency command has changed, the acceleration control unit 751 proceeds to step S206, and if the frequency command has not changed, it repeats the process of step S204.

In step S206, the acceleration control unit 751 determines whether the change in the frequency command is steep. If the change in the frequency command is steep (see FIG. 11), the acceleration control unit 751 proceeds to step S206, and if the change is not steep (see FIG. 12), the process proceeds to step S208.

In step S208, the acceleration control unit 751 determines whether the actual rotational frequency of the electric motor EM is changing. The actual rotational frequency of the electric motor EM is obtained based on, for example, a detected value or an estimated value of the rotational speed of the electric motor EM, which is input from the control circuit 70 to the diagnostic device 75. When the actual rotational frequency of the electric motor EM is changing (see FIG. 11), the acceleration control unit 751 determines that the electric motor EM is in an acceleration state suitable for diagnosis, and ends the processing of the current flowchart, and The process moves to step S104 in FIG. On the other hand, if the actual rotational frequency of the electric motor EM has not changed, the acceleration control unit 751 determines that the electric motor EM is not in an acceleration state suitable for diagnosis, and returns to step S204.

In step S210, the acceleration control unit 751 determines whether the actual rotational frequency of the electric motor EM follows the frequency command. If the actual rotational frequency of the electric motor EM follows the frequency command (see FIG. 12), the acceleration control unit 751 determines that the electric motor EM is in an acceleration state suitable for diagnosis, and ends the processing of the current flowchart. At the same time, the process moves to step S104 in FIG. On the other hand, if the actual rotational frequency of the electric motor EM does not follow the frequency command, the acceleration control unit 751 determines that the electric motor EM is not in an acceleration state suitable for diagnosis, and returns to step S204.

Returning to FIG. 9, in step S104, the filter unit 752 performs filter processing while the electric motor EM is accelerating. Whether or not electric motor EM is accelerating may be determined based on a signal from acceleration control section 751. Specifically, as described above, the filter unit 752 determines the specific frequency of the waveform data of the d-axis and q-axis current detection values based on the d-axis and q-axis current detection values that are sequentially input from the control circuit 70. Extract component data and output time series data for multiple analysis targets.

When the electric motor EM shifts from the acceleration state to the steady state and the process of step S104 is completed, the diagnostic device 75 shifts to the process of step S106.

In step S106, the peak and valley detection unit 753 performs peak and valley detection processing. Specifically, as described above, the peak and valley detection unit 753 detects local maximum values and local minimum values (peak values) for each of the time series data of a plurality of analysis targets, and outputs time series data of the peak values.

Upon completion of the process in step S106, the diagnostic device 75 proceeds to step S108.

In step S108, the waveform counting unit 754 performs waveform counting processing. Specifically, as described above, the waveform counting unit 754 calculates the amplitude of each of the plurality of waveforms included in the time-series data to be analyzed based on the time-series data of the peak value for each of the time-series data to be analyzed. Get the value and output it.

Upon completion of the process in step S108, the diagnostic device 75 proceeds to step S110.

In step S110, the amplitude analysis unit 755 performs amplitude analysis processing. Specifically, as described above, the amplitude analysis unit 755 creates and outputs a frequency distribution of amplitude values for each of a plurality of waveforms included in the time series data to be analyzed, for each time series data to be analyzed. .

Upon completion of the process in step S110, the diagnostic device 75 proceeds to step S112.

In step S112, the feature amount acquisition unit 756 performs feature amount acquisition processing. Specifically, the feature acquisition unit 756 acquires information regarding the abnormality of the electric motor EM based on the frequency distribution of amplitude values for each of the plurality of waveforms included in the time series data to be analyzed, for each of the time series data to be analyzed. Get features.

Upon completion of the process in step S112, the diagnostic device 75 proceeds to step S114.

In step S114, the external factor extraction unit 757 performs external factor extraction processing. Specifically, as described above, the external factor extraction unit 757 extracts and outputs the feature amount that includes the influence of the external factor from among the feature amounts for each of the time series data of the plurality of analysis targets.

Upon completion of the process in step S114, the diagnostic device 75 proceeds to step S116.

In step S116, the diagnostic unit 758 performs diagnostic processing. For example, the diagnostic unit 758 moves to the subflowchart of FIG. 13.

In step S302, the diagnosis unit 758 determines whether a feature amount including an external factor was extracted in step S114. If the feature value including the external factor is not extracted in step S114, the diagnostic unit 758 proceeds to step S304, and if it is extracted, the process proceeds to step S306.

In step S304, the diagnosis unit 758 determines to use all the feature amounts acquired in step S110 as the feature amounts to be considered.

Upon completion of the process in step S304, the diagnostic unit 758 proceeds to step S310.

On the other hand, in step S306, the diagnosis unit 758 determines whether the number of feature quantities including the influence of external factors extracted by the external factor extraction unit 757 is equal to or greater than a predetermined number. The predetermined number may be a fixed value, or may be varied so as to increase as the number of time-series data to be analyzed increases. If the number of features including the influence of external factors is not greater than or equal to the predetermined number, the diagnostic unit 758 proceeds to step S308, and if the number of features including the influence of external factors is greater than or equal to the predetermined number, the procedure proceeds to step S316.

In step S308, the diagnosis unit 758 determines to use all the feature quantities obtained in step S110, excluding the feature quantity extracted in step S112, as the feature quantity to be considered.

Upon completion of the process in step S308, the diagnostic unit 758 proceeds to step S310.

In step S310, the diagnostic unit 758 determines whether the feature amount for each time series data of the plurality of analysis targets changes in a predetermined manner in response to a change in the rotational frequency (range) of the electric motor EM. If the feature amount for each time-series data of the plurality of analysis targets does not change in a predetermined manner in response to changes in the rotational frequency (range) of the electric motor EM, the diagnostic unit 758 proceeds to step S312 and performs a predetermined change. If so, the process advances to step S314.

In step S312, the diagnostic unit 758 outputs a diagnostic result indicating that the electric motor EM is normal.

On the other hand, in step S314, the diagnostic unit 758 outputs a diagnostic result indicating that the electric motor EM is abnormal.

In step S316, the diagnosis unit 758 outputs a diagnosis result indicating that diagnosis regarding the abnormality of the electric motor EM cannot be performed.

When the diagnosis unit 758 completes any one of steps S312, S314, and S316, it ends the process of the current flowchart and proceeds to the process of step S118 in FIG. 9.

In step S118, the notification unit 759 performs notification processing. Specifically, the notification unit 759 may notify the user of the diagnosis result by causing the display unit 80 to display information regarding the diagnosis result of the diagnosis process in step S116, as described above. In addition, instead of this, or in addition, the notification unit 759 transmits information regarding the diagnosis result obtained by the diagnosis process in step S116 to the management device 200 and the terminal device 300 through the communication unit 90, thereby informing the user of the diagnosis result. may be notified.

When the process of step S118 is completed, the diagnostic device 75 ends the process of the current flowchart.

In this manner, in this example, the diagnostic device 75 diagnoses the abnormality of the electric motor EM based on time-series data of a plurality of analysis targets corresponding to mutually different rotational speed ranges of the electric motor EM.

As a result, the diagnostic device 75 can detect, for example, even if the time-series data to be analyzed is affected by an external factor in a certain frequency range, the time-series data to be analyzed can be It is possible to separate (eliminate) the influence of external factors. Therefore, the diagnostic device 75 can improve the accuracy of diagnosis regarding abnormalities in the electric motor EM.

[Other embodiments]
Next, other embodiments will be described.

The embodiments described above may be modified or changed as appropriate.

For example, in the embodiment described above, the diagnostic device 75 may acquire physical quantity data regarding the operation of the electric motor EM during deceleration rather than during acceleration.

Furthermore, in the embodiment described above, the diagnostic device 75 may perform the process of the flowchart of FIG. 9 described above in accordance with the timing at which the electric motor EM accelerates or decelerates according to the control by the control circuit 70. For example, the diagnostic device 75 executes the flowchart of FIG. 9 described above when the electric motor EM is started or stopped. Furthermore, in the case of operating conditions in which the rotational speed of the electric motor EM changes, the diagnostic device 75 may execute the flowchart of FIG. 9 described above in accordance with the timing at which the rotational speed of the electric motor EM changes.

Further, in the above-described embodiments and examples of modifications and changes thereof, the diagnostic device 75 determines whether the difference between the command value and the actual value (detected value or estimated value) of the rotational speed of the electric motor EM is relative to a predetermined standard. Diagnosis regarding an abnormality in the electric motor EM may be performed using data during acceleration or deceleration of the electric motor in a small state. Thereby, it is possible to suppress a situation where the accuracy of diagnosing a mechanical abnormality of the electric motor EM deteriorates. In this case, for example, in the flowchart of FIG. 10 described above, after the processing of steps S208 and S210, it is determined whether the difference between the frequency command of the electric motor EM and the actual rotational frequency is equal to or less than a predetermined standard that can be determined to be substantially the same. A process is provided to determine.

Furthermore, in the above-described embodiment and its modification/change examples, the diagnostic device 75 may diagnose an abnormality in the electric motor EM using the α-axis and β-axis current detection values of the electric motor EM. In this case, the values calculated in the process of conversion into the d-axis and q-axis current detection values of the vector conversion unit 703 are used, and the α-axis and β-axis current detection values of the electric motor EM are calculated by the filter unit 752. is input.

Furthermore, in the above-described embodiments and examples of modifications and changes thereof, the diagnostic device 75 may diagnose abnormalities in the electric motor EM using the detected current values of the U-phase, V-phase, and W-phase of the electric motor EM. good. In this case, the detected current values of the U-phase, V-phase, and W-phase of the electric motor EM are input to the filter section 752.

Furthermore, in the above-described embodiments and examples of modifications and changes thereof, the diagnostic device 75 detects detected values and estimated values of the voltage, rotational speed, torque, etc. of the electric motor EM instead of or in addition to the current of the electric motor EM. Diagnosis regarding an abnormality in the electric motor EM may be performed using the following method. That is, the physical quantity related to the operation of the electric motor EM may be the voltage, rotational speed, torque, etc. of the electric motor EM. In this case, data of detected values and estimated values of the voltage, rotational speed, torque, etc. of the electric motor EM is input to the filter section 752.

Further, in the above-described embodiments and examples of modifications and changes thereof, the diagnostic device 75 uses frequency domain data instead of or in addition to time domain data (waveform data) of physical quantities related to the operation of the electric motor EM. Then, a diagnosis regarding an abnormality in the electric motor EM may be performed. For example, the diagnostic device 75 performs FFT (Fast Fourier Transform) analysis on each of a plurality of pieces of data of physical quantities related to the operation of the electric motor EM that correspond to mutually different rotational speeds (ranges), and identifies specific frequency components for each of the plurality of pieces of data. Obtain the spectral value of Then, the diagnostic device 75 diagnoses the abnormality of the electric motor EM based on the spectrum value (feature amount) of the specific frequency component for each of the plurality of pieces of data. At this time, the diagnostic device 75 (external factor extraction unit 757) can extract spectral values that include the influence of external factors by comparing the spectral values of specific frequency components for each of the plurality of pieces of data.

Further, in the above-described embodiment and its modification/modification examples, the diagnostic device 75 changes the rotational speed of the electric motor EM in stages and collects a plurality of data of physical quantities related to the operation of the electric motor EM, which have mutually different rotational speeds. You may obtain it.

Further, for example, in the above-described embodiments and modifications thereof, the power conversion device 100 drives the electric motor EM using power input from a DC power supply instead of three-phase AC power input from the commercial power supply PS. Electric power may be generated and output. In this case, for example, a DC voltage input from a DC power supply is applied between the positive line PL and the negative line NL. Further, in this case, the rectifier circuit 10 may be omitted.

Further, for example, in the above-described embodiments and modifications thereof, the power conversion device 100 directly transfers three-phase AC power of the R phase, S phase, and T phase to the three-phase AC power of the U phase, V phase, and W phase. A matrix converter capable of converting into phase alternating current power may also be used.

Further, for example, in the above-described embodiments and modifications, part or all of the functions of the diagnostic device 75 may be transferred to the outside of the power conversion device 100. For example, some or all of the functions of the diagnostic device 75 may be transferred to the management device 200 or the terminal device 300. In this case, data necessary for diagnosing an abnormality in the electric motor EM is transmitted (uploaded) to an external device such as the management device 200 or the terminal device 300 through the communication unit 90.

[Effect]
Next, the operation of the diagnostic system 1 according to this embodiment will be explained.

In this embodiment, the diagnostic device includes a first acquisition section, a second acquisition section, and a diagnosis section. The diagnostic device is, for example, the diagnostic device 75 described above. The first acquisition unit is, for example, the filter unit 752 described above. The second acquisition unit is, for example, the feature amount acquisition unit 756 described above. The diagnosis unit is, for example, the diagnosis unit 758 described above. Specifically, the first acquisition unit acquires physical quantity data regarding the operation of the electric motor. The electric motor is, for example, the electric motor EM described above. The physical quantity data related to the operation of the electric motor is, for example, data on detected values or estimated values of the electric motor's current, voltage, rotational speed, torque, etc., as described above. Further, the second acquisition unit acquires, based on the data acquired by the first acquisition unit, a feature quantity related to a component of a specific frequency that has a correlation with the rotational frequency of the electric motor in the waveform data of the physical quantity related to the operation of the electric motor. Then, the diagnosis section diagnoses the abnormality of the electric motor based on the feature amounts corresponding to each of the plurality of data obtained by the first acquisition section and having different rotational speeds or rotational speed ranges of the electric motor. The plurality of data is, for example, the time-series data of the plurality of analysis targets described above.

Thereby, the diagnostic device can utilize feature amounts for each of a plurality of pieces of data having mutually different rotational frequencies (ranges). Therefore, in the diagnostic device, the rotational frequency (range) of the electric motor when some data is acquired overlaps with the frequency (range) related to external factors, and as a result, some feature values are affected by external factors. Even if the influence of Therefore, the diagnostic device can suppress erroneous diagnosis due to the influence of external factors and improve the accuracy of diagnosis regarding abnormality of the electric motor.

Furthermore, in the present embodiment, the diagnostic unit diagnoses the abnormality of the electric motor based on the feature amounts corresponding to each of the plurality of data acquired by the first acquisition unit during acceleration or deceleration of the electric motor. Good too.

Thereby, the diagnostic device can acquire a plurality of data in mutually different rotational frequency ranges.

Further, in the present embodiment, the diagnostic unit detects the electric motor in a state where the difference between the command value and the actual value of the rotational speed of the electric motor acquired by the first acquisition unit is relatively small with respect to a predetermined standard. Diagnosis regarding an abnormality of the electric motor may be performed based on feature amounts corresponding to each of a plurality of pieces of data during acceleration or deceleration.

Thereby, the diagnostic device can suppress a decrease in accuracy when diagnosing a mechanical abnormality of the electric motor, such as a bearing abnormality, by using data on an electrical physical quantity such as an electric current, for example.

Further, in this embodiment, the diagnostic device may include a third acquisition section and a fourth acquisition section. The third acquisition unit is, for example, the waveform counting unit 754 described above. The fourth acquisition unit is, for example, the amplitude analysis unit 755 described above. Specifically, the first acquisition unit may extract data of a specific frequency component from waveform data of a physical quantity related to the operation of the electric motor. Further, the third acquisition unit may acquire amplitude values for each of a plurality of time-series waveforms included in the data of the specific frequency component. Further, the fourth acquisition unit may acquire the frequency distribution of the above-mentioned amplitude values. Then, the second acquisition unit may acquire the above-mentioned feature amount based on the above-mentioned frequency distribution.

Thereby, the diagnostic device can acquire a feature amount that is correlated with the abnormality of the electric motor.

Further, in the present embodiment, the feature amount may be acquired based on the frequency value of a section in the frequency distribution in which the amplitude value is relatively large with respect to a predetermined standard.

Thereby, the diagnostic device can acquire a feature amount that is correlated with the abnormality of the electric motor.

Further, in this embodiment, the diagnostic device may include a first extraction section. The first extraction unit is, for example, a peak/trough detection unit 753. Specifically, the first extraction unit may extract the minimum value and maximum value of the data of the specific frequency component. The third acquisition unit may acquire amplitude values for each of the plurality of time-series waveforms included in the data of the specific frequency component based on the minimum value and maximum value extracted by the first extraction unit. good.

Thereby, the diagnostic device can more easily acquire the feature amount for each of the plurality of waveforms included in the data of the specific frequency component in the waveform data of the physical quantity related to the operation of the electric motor.

Further, in the present embodiment, the first acquisition unit may extract data of a specific frequency component from the waveform data of the physical quantity so as to follow a change in the rotational frequency of the electric motor. The first acquisition unit includes, for example, the above-mentioned tracking filter TF.

Thereby, the diagnostic device can appropriately acquire data on a component of a specific frequency that has a correlation with the rotational frequency of the electric motor from data on physical quantities related to the operation of the electric motor during acceleration and deceleration.

Further, in this embodiment, the diagnostic device may include a second extractor. The second extraction unit is, for example, the external factor extraction unit 757 described above. Specifically, the second extraction unit may extract the feature amount including the influence of external factors by comparing the feature amount corresponding to each of the plurality of data. The diagnosis section may diagnose the abnormality of the electric motor based on the feature amounts excluding the feature amount extracted by the second extraction section among the feature amounts corresponding to each of the plurality of data.

Thereby, the diagnostic device can isolate the influence of external factors and diagnose abnormalities in the electric motor.

Further, in the present embodiment, the second extraction unit selects a feature amount that exceeds a predetermined standard with respect to a tendency of change in the feature amount according to a change in the rotational frequency of the electric motor, from among the feature amounts corresponding to each of the plurality of data. deviating features may be extracted.

Thereby, the diagnostic device can appropriately extract a feature amount including the influence of an external factor, taking into account the characteristics of the influence of an external factor that affects at a fixed frequency without depending on the rotation frequency.

Furthermore, in the present embodiment, if the number of feature quantities extracted by the second extraction unit is relatively large with respect to a predetermined standard, the diagnosis unit determines that it is not possible to diagnose the abnormality of the electric motor. Good too.

As a result, the diagnostic device can suppress incorrect diagnosis in situations where, for example, there are relatively many frequency ranges where the influence of external factors appears on waveform data of physical quantities related to the operation of an electric motor. .

Furthermore, in the present embodiment, the diagnostic unit may diagnose an abnormality in the electric motor based on a tendency of change in a feature amount corresponding to each of a plurality of pieces of data in accordance with a change in the rotational frequency of the electric motor.

Thereby, the diagnostic device can appropriately diagnose the abnormality of the electric motor by utilizing, for example, the tendency of the feature amount to change depending on the rotational frequency when the electric motor is abnormal.

Further, in this embodiment, the diagnostic device may be installed in a power conversion device that drives an electric motor.

Thereby, the driving function and diagnostic function of the electric motor can be integrated into the power conversion device.

Although the embodiments have been described in detail above, the present disclosure is not limited to such specific embodiments, and various modifications and changes can be made within the scope of the gist described in the claims.

1 Diagnostic system 10 Rectifier circuit 20 Smoothing circuit 21 Smoothing capacitor 30 Inverter circuit 40 Current sensor 50 Voltage sensor 60 Gate drive circuit 70 Control circuit 75 Diagnostic device 80 Display section 90 Communication section 100 Power conversion device 150 Rotation state sensor 200 Management device 300 Terminal Device 701 Speed adjustment section 702 Current detection section 703 Vector conversion section 704 Current adjustment section 705 Vector inverse conversion section 706 Voltage compensation section 707 Gate signal output section 751 Acceleration control section 752 Filter section 753 Peak and valley detection section 754 Waveform counting section 755 Amplitude analysis section 756 Feature acquisition unit 757 External factor extraction unit 758 Diagnosis unit 759 Notification unit EM Motor NL Negative line PL Positive line PS Commercial power supply SD Semiconductor diode SW Semiconductor switch TF Tracking filter TF1 Rotating coordinate conversion unit TF2 Low-pass filter unit TF3 Fixed coordinate conversion Department

Claims (13)

a first acquisition unit that acquires data on physical quantities related to the operation of the electric motor;
a second acquisition unit that acquires, based on the data acquired by the first acquisition unit, a feature quantity related to a component of a specific frequency that has a correlation with the rotational frequency of the electric motor in the waveform data of the physical quantity;
a diagnosis unit that diagnoses an abnormality of the electric motor based on the feature amount corresponding to each of a plurality of data in which the rotational speed or rotational speed range of the electric motor is different from each other, acquired by the first acquisition unit; prepare,
Diagnostic equipment. The diagnosis unit diagnoses an abnormality of the electric motor based on the feature amount corresponding to each of the plurality of data during acceleration or deceleration of the electric motor, which is acquired by the first acquisition unit.
The diagnostic device according to claim 1. The diagnostic unit is configured to detect acceleration of the electric motor in a state where a difference between a command value and an actual value of the rotational speed of the electric motor, which are acquired by the first acquisition unit, is relatively small with respect to a predetermined standard. or diagnosing an abnormality of the electric motor based on the feature amount corresponding to each of the plurality of data during deceleration;
The diagnostic device according to claim 2. a third acquisition unit;
a fourth acquisition unit;
The first acquisition unit extracts data of the specific frequency component from the waveform data of the physical quantity,
The third acquisition unit acquires amplitude values for each of a plurality of time-series waveforms included in the data of the specific frequency component,
The fourth acquisition unit acquires a frequency distribution of the amplitude values,
the second acquisition unit acquires the feature amount based on the frequency distribution;
The diagnostic device according to claim 1. The feature quantity is obtained based on a frequency value of a section in the frequency distribution in which the amplitude value is relatively large with respect to a predetermined standard.
The diagnostic device according to claim 4. comprising a first extraction unit that extracts the minimum value and maximum value of the data of the specific frequency component,
The third acquisition unit acquires the amplitude value for each of the plurality of time-series waveforms included in the data of the specific frequency component, based on the minimum value and maximum value extracted by the first extraction unit. do,
The diagnostic device according to claim 4. The first acquisition unit extracts data of the specific frequency component from the waveform data of the physical quantity so as to follow changes in the rotational frequency of the electric motor.
The diagnostic device according to claim 2. a second extraction unit that extracts the feature amount including the influence of external factors by comparing the feature amount corresponding to each of the plurality of data;
The diagnosis unit diagnoses an abnormality of the electric motor based on the feature quantities excluding the feature quantity extracted by the second extraction unit among the feature quantities corresponding to each of the plurality of data.
The diagnostic device according to claim 1. The second extraction unit extracts, from among the feature quantities corresponding to each of the plurality of data, a tendency of change in the feature quantity in response to a change in the rotational frequency of the electric motor that deviates beyond a predetermined standard. extracting the feature amount that is
The diagnostic device according to claim 8. The diagnosis unit determines that a diagnosis regarding an abnormality of the electric motor cannot be performed when the number of the feature quantities extracted by the second extraction unit is relatively large with respect to a predetermined standard.
The diagnostic device according to claim 8. The diagnostic unit diagnoses an abnormality in the electric motor based on a tendency of change in the feature amount corresponding to each of the plurality of data in accordance with a change in rotational frequency of the electric motor.
The diagnostic device according to claim 1. installed in a power conversion device that drives the electric motor,
A diagnostic device according to any one of claims 1 to 11. a first acquisition step of acquiring physical quantity data regarding the operation of the electric motor;
a second acquisition step of acquiring, based on the data acquired in the first acquisition step, a feature quantity related to a component of a specific frequency that has a correlation with the rotational frequency of the electric motor in the waveform data of the physical quantity;
a diagnosis step of diagnosing an abnormality of the electric motor based on the feature amount corresponding to each of a plurality of data in which the rotational speed or rotational speed range of the electric motor differs from each other, acquired in the first acquisition step; include,
Diagnostic method.

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