JP6824493B1 - Diagnostic device for electric motor - Google Patents

Abstract

An electric motor including a current detection circuit (7) for detecting the current of the electric motor (5), an arithmetic processing unit (10) for arithmetically processing the detected current to detect an abnormality of the electric motor, and a storage unit (11). In the diagnostic apparatus (100) of the above, the arithmetic processing unit (10) has an effective value calculation unit (21) for calculating the effective value of the current, and the signal strength of the side band wave to the specific frequency band is obtained by the current FFT analysis in advance. The peak value of is extracted and stored in the storage unit (11) in association with the current effective value at that time, and the threshold value set for the peak value of the signal strength in the specific frequency band is stored in the storage unit (11) in advance. The peak value of the signal strength of the specific frequency band based on the current detected at the time of diagnosis of the electric motor (5) is stored in the storage unit (11) in advance, and the peak value of the signal strength of the specific frequency band for each effective current value is stored. And the abnormality of the electric motor is determined by comparing with the threshold value.

Description

The present application relates to a diagnostic device for an electric motor.

Conventionally, the load current of an induction motor is measured and frequency analysis is performed, focusing on the sidebands generated on both sides of the operating frequency, and the short-period vertical waveform disturbance and the long-period vertical waveform. A method for diagnosing an abnormality of an induction motor and equipment for diagnosing an abnormality of a device driven by the induction motor based on the state of swell, which is the vibration of the frequency, has been proposed (for example, Patent Document 1).

In the conventional equipment abnormality diagnosis method, when the load torque of the induction motor fluctuates, the spectral intensities on both sides near the power supply frequency (operating frequency) increase, and side bands that occur in peaks on both sides of the power supply frequency. There is a problem that it becomes larger than the vibration intensity of the wave and it is difficult to detect the sideband wave.

On the other hand, the applicant has applied for an electric motor diagnostic device capable of diagnosing the presence or absence of an abnormality in the electric motor by detecting a period in which the load does not fluctuate even in the electric motor in which the load torque fluctuates (for example, a patent). Document 2).

Japanese Patent No. 4782218 Japanese Patent No. 6190841

However, recently, it is expected that the accuracy of the abnormality diagnosis of the electric motor will be further improved. For that purpose, the detection accuracy is required to be the same as when the load fluctuation does not occur when the load fluctuation occurs.

The present application discloses a technique for solving the above-mentioned problems, and an object of the present application is to provide an electric motor diagnostic device capable of determining an abnormality occurrence of an electric motor without receiving fluctuations in a load. ..

Diagnostic apparatus for an electric motor according to the present disclosure, processing a voltage detection circuit for detecting a current detection circuit and voltage, a voltage detected by said current detected by the detection circuit current and the voltage detection circuit detects the current of the electric motor A diagnostic device for an electric motor including an arithmetic processing unit for detecting an abnormality of the electric motor and a storage unit for storing the calculation result of the arithmetic processing unit, and the arithmetic processing unit detects the current detection circuit. A torque calculation unit that calculates a torque value from the generated current and a voltage detected by the voltage detection circuit, a state determination unit that determines whether the calculated torque value is in a stable state, and a state determination unit that is detected by the current detection circuit. The analysis unit has an analysis unit that extracts the peak value of the signal strength of a specific frequency band from the side band wave by FFT analysis of the current, and an abnormality determination unit that determines whether an abnormality has occurred in the electric motor. The peak value of the signal strength of the specific frequency band and the torque value at that time are stored in the storage unit in advance, and the peak value of the signal strength of the specific frequency band from which the threshold value for setting the normal range of the electric motor is extracted is stored. The threshold value is stored in the storage unit in advance, and the abnormality determination unit sets the peak value of the signal strength of the specific frequency band obtained by FFT analysis of the current detected by the current detection circuit. The presence or absence of an abnormality in the electric motor is determined by comparing the peak value of the signal strength of the specific frequency band for each torque value stored in the storage unit in advance with the threshold value stored in the storage unit in advance.

According to the electric motor diagnostic device of the present disclosure, it is possible to determine the occurrence of an abnormality in the electric motor without receiving fluctuations in the load.

It is a figure which shows the schematic structure and the installation state of the diagnostic apparatus of the electric motor which concerns on Embodiment 1. FIG. It is a figure which shows the structure of the arithmetic processing part of the diagnostic apparatus of the electric motor which concerns on Embodiment 1, and is the figure for demonstrating the signal flow at the time of learning using the current analysis. It is a figure which shows the structure of the arithmetic processing part of the diagnostic apparatus of the electric motor which concerns on Embodiment 1, and is the figure for demonstrating the signal flow at the time of diagnosis using the current analysis. It is a flowchart which shows the procedure of performing a diagnosis using the diagnostic apparatus of the electric motor which concerns on Embodiment 1. FIG. It is a flowchart which shows the procedure of performing a diagnosis using the diagnostic apparatus of the electric motor which concerns on Embodiment 1. FIG. It is a figure for demonstrating the effect of the diagnostic apparatus of the electric motor which concerns on Embodiment 1. FIG. It is a figure which shows the schematic structure and the installation state of the diagnostic apparatus of the electric motor which concerns on Embodiment 2. It is a figure which shows the structure of the arithmetic processing part of the diagnostic apparatus of the electric motor which concerns on Embodiment 2, and is the figure for demonstrating the signal flow at the time of learning of the current-voltage analysis. It is a figure which shows the structure of the arithmetic processing part of the diagnostic apparatus of the electric motor which concerns on Embodiment 2, and is the figure for demonstrating the signal flow at the time of diagnosis of the current-voltage analysis. It is a flowchart which shows the procedure which performs the diagnosis using the diagnostic apparatus of the electric motor which concerns on Embodiment 2. It is a flowchart which shows the procedure which performs the diagnosis using the diagnostic apparatus of the electric motor which concerns on Embodiment 2. It is a figure for demonstrating the effect of the diagnostic apparatus of the electric motor which concerns on Embodiment 2. FIG. It is a hardware block diagram of the diagnostic apparatus of the electric motor which concerns on embodiment.

Hereinafter, the present embodiment will be described with reference to the drawings. In each figure, the same reference numerals indicate the same or corresponding parts.

Embodiment 1.
Hereinafter, the diagnostic device for the electric motor according to the first embodiment will be described with reference to the drawings.
FIG. 1 is a diagram showing a schematic configuration and an installation status of a diagnostic device for an electric motor according to the first embodiment. The electric motor diagnostic device according to the first embodiment is mainly used in a control center which is a closed switchboard. In the figure, the main circuit 1 drawn from the power system is provided with a wiring breaker 2, an electromagnetic contactor 3, an instrument transformer 4 for detecting the load current of the main circuit 1, and the like. An electric motor 5 such as a three-phase induction motor, which is a load, is connected to the main circuit 1, and the mechanical equipment 6 is driven by the electric motor 5. The electric motor diagnostic device 100 is connected to the instrument transformer 4 and performs a predetermined calculation based on the outputs of the current detection circuit 7 and the current detection circuit 7 that detect the load current of the main circuit 1 and convert it into a predetermined signal. It is provided with an arithmetic processing unit 10 and the like.

The storage unit 11 is connected to the setting circuit 12 and the arithmetic processing unit 10 and exchanges data with the arithmetic processing unit 10.
The setting circuit 12 is a circuit for setting the power supply frequency, the rated output of the electric motor, the rated voltage, the rated current, the number of poles, the rated rotation frequency, and the like, and stores these information in the storage unit 11.
The display unit 13 is connected to the arithmetic processing unit 10 and displays a detected physical quantity such as a load current and an abnormal state, a warning, or the like when the arithmetic processing unit 10 detects an abnormality in the electric motor 5.
The drive circuit 14 is connected to the arithmetic processing unit 10 and outputs a control signal for opening and closing the magnetic contactor 3 based on the output of the arithmetic processing unit 10 based on the current detected by the instrument transformer 4.
The external output unit 15 outputs an abnormal state and an alarm to the outside in response to the output from the arithmetic processing unit 10.
The external monitoring device 200 is composed of a PC (personal computer) or the like and is connected to the diagnostic device 100 of one or a plurality of electric motors. The output of the arithmetic processing unit 10 is appropriately received via the communication circuit 16 and is appropriately received. The operating status of the diagnostic device 100 of the electric motor is monitored. The connection between the external monitoring device 200 and the communication circuit 16 of the electric motor diagnostic device 100 may be by using a cable or by wireless. A network may be formed between the diagnostic devices 100 of the plurality of electric motors and the connection may be made via the Internet.

Next, the configuration of the arithmetic processing unit 10 will be described. 2 and 3 are diagrams showing the configuration of the arithmetic processing unit 10, FIG. 2 is a diagram for explaining a signal flow during learning using current analysis, and FIG. 3 is a diagram for diagnosing using current analysis. It is a figure for demonstrating the flow of a signal.
In FIGS. 2 and 3, the arithmetic processing unit 10 includes a current conversion unit 20, a state determination unit 30, an analysis unit 40, and an abnormality determination unit 50, and is a current and specific frequency band storage device 60 in which a current and a specific frequency are stored. , Operates in cooperation with a storage unit 11 having a threshold storage device 61 in which a threshold is stored.

First, the signal flow during learning using current analysis will be described with reference to FIG.
The current conversion unit 20 inputs a predetermined current signal converted by the current detection circuit 7, and the effective value calculation unit 21 calculates the current effective value. The calculated current execution value is determined by the stable state determination unit 31 of the state determination unit 30 whether or not it is in the stable state. Here, the stable state means that the current effective value is constant for a certain period of time. In addition, the fixed time is a predetermined time.

When it is determined that the current effective value is in a stable state, the current of the storage unit 11 and the current effective value are stored in the specific frequency band storage device 60, and the frequency analysis unit 41 of the analysis unit 40 stores the current FFT ( Fast Fourier Transform) analysis is performed. The FFT analysis result is averaged by the averaging analysis unit 42. Noise can be reduced by this averaging process.
The sideband wave analysis unit 43 extracts sideband waves near the power supply frequency from the signal subjected to the averaging process.

Next, the specific frequency band detection unit 44 detects a specific frequency band caused by a mechanical system abnormality. The specific frequency band caused by the mechanical system abnormality detected is, for example, a specific frequency band caused by the rotation frequency (rotation frequency band), a specific frequency band caused by the rotor bar abnormality, a specific frequency band caused by the belt rotation frequency, or the like.
Then, the signal strength of the specific frequency band and the current effective value when the signal strength is calculated are stored in the current of the storage unit 11 and the specific frequency band storage device 60. Here, the signal strength of a specific frequency band is stored for each effective current value. That is, the normal range can be determined for each effective current value.

Using the current and the signal strength of the specific frequency band for each effective current value stored in the specific frequency band storage device 60, the normal range analysis unit 45 calculates the distribution of the normal range for each effective current value. Here, for example, the standard deviation σ is calculated by statistical processing and determined with 3σ as the threshold value. The threshold value set to be the normal range in the normal range analysis unit 45 is stored in the threshold storage device 61 of the storage unit 11.
The threshold value is not limited to 3σ, and the threshold value can be determined by statistical processing for each current value. Alternatively, a certain current value can be used as a reference, and a correction coefficient can be added to the current value to obtain a threshold value. For example, the signal strength of the specific frequency band at the time of the rated current is used as a reference, and the signal strength of the specific frequency band at the time of the current other than the rated current is corrected. At the time of diagnosis described later, when the rated current is used, the stored threshold value is used as it is, but when the current is other than the rated current, a value obtained by adding a correction to the stored threshold value is set as the threshold value. The threshold value set to be the normal range in the normal range analysis unit 45 is stored in the threshold storage device 61 of the storage unit 11.

Next, the signal flow at the time of diagnosis using the current analysis will be described with reference to FIG.
Similar to the time of learning, the current conversion unit 20 inputs a predetermined current signal converted by the current detection circuit 7, and the effective value calculation unit 21 calculates the current effective value. The calculated current execution value is determined by the stable state determination unit 31 of the state determination unit 30 whether or not it is in the stable state.
When it is determined that the current effective value is in a stable state, the frequency analysis unit 41 of the analysis unit 40 performs the current FFT analysis. The FFT analysis result is averaged by the averaging analysis unit 42.
The sideband wave analysis unit 43 extracts sideband waves near the power supply frequency from the signal subjected to the averaging process.

Next, the specific frequency band detection unit 44 detects a specific frequency band caused by a mechanical system abnormality. The specific frequency band caused by the mechanical system abnormality detected is, for example, a specific frequency band caused by the rotation frequency (rotation frequency band), a specific frequency band caused by the rotor bar abnormality, a specific frequency band caused by the belt rotation frequency, or the like.
The specific frequency band detected by the specific frequency band detection unit 44 is input to the abnormality determination unit 50. The abnormality determination unit 51 stores the specific frequency band, the current, and the signal strength of the specific frequency band for each effective current value stored in the specific frequency band storage device 60, which is detected by the specific frequency band detection unit 44, in the threshold storage device. The data of the set threshold is input. The abnormality determination unit 51 compares the signal strength of the specific frequency band detected by the specific frequency band detection unit 44 with the current and the signal strength of the specific frequency band for each effective current value stored in the specific frequency band storage device 60. , It is determined whether or not the detected specific frequency band is a specific frequency band caused by a mechanical system abnormality, and whether or not it is within the normal range, that is, whether or not an abnormality has occurred is determined by using the threshold value for each effective current value. The determination result is output from the abnormality determination unit 50.

4A and 4B are flowcharts showing a procedure for performing a diagnosis using the diagnostic device for the electric motor according to the first embodiment. Here, the detection of the rotation frequency band as a specific frequency band will be described as an example. The diagnostic device for the electric motor according to the first embodiment shifts to a diagnostic period that can be diagnosed after a predetermined learning period.
First, the learning period will be explained.
In step S101, the current waveform is acquired. Specifically, the current detection circuit 7 connected to the instrument transformer 4 detects the load current of the main circuit 1 and converts it into a predetermined signal.
In step S102, the effective value calculation unit 21 calculates the current effective value.
In step S103, the stable state determination unit 31 determines whether or not the current effective value is in the stable state. If it is not in a stable state (No in step S103), the process returns to step S101. If it is in a stable state (Yes in step S103), the process proceeds to step S104, and the calculated current effective value is stored in the current and the specific frequency band storage device 60.

Next, in step S105, the frequency analysis unit 41 executes the current FFT analysis, and in step S106, the averaging analysis unit 42 averages the results of the analyzed current FFT. Noise can be reduced by this averaging process.
In step S107, the sideband wave is extracted from the result of the current FFT averaged by the sideband wave analysis unit 43. In step S108, the peak of the rotation frequency band is extracted from the side band waves extracted by the specific frequency band detection unit 44, and the peak value of the signal intensity of the extracted rotation frequency band is stored in the current and the specific frequency band in step S109. Store in device 60. The current and the specific frequency band storage device 60 stores the current effective value and the peak value of the signal strength in the rotation frequency band in relation to each other (step S110).

The flow up to step S110 is the flow of the learning period. During the learning period, steps S101 to S110 are repeated (No in step S111). When the learning period is completed by repeating the process a plurality of times (Yes in step S111), the diagnosis period is reached.

In the diagnosis period, first, in step S112, the current waveform is acquired. Similar to the learning period, the current detection circuit 7 connected to the instrument transformer 4 detects the load current of the main circuit 1 and converts it into a predetermined signal.
In step S113, the effective value calculation unit 21 calculates the current effective value.
In step S114, the stable state determination unit 31 determines whether or not the current effective value is in the stable state. If the current effective value is not stable, the process returns to step S112 and the current waveform is acquired (No in step S112). When the current effective value is in a stable state, the process proceeds to step S115, and the frequency analysis unit 41 executes the current FFT analysis.

Next, in step S116, the averaging analysis unit 42 averages the analysis result of the current FFT. Noise can be reduced by this averaging process.
In step S117, the sideband wave is extracted from the analysis result of the current FFT averaged by the sideband wave analysis unit 43.
In step S118, the peak of the rotation frequency band is extracted from the side band waves extracted by the specific frequency band detection unit 44.

In step S119, the abnormality determination unit 51 determines the signal strength of the rotation frequency band associated with the peak value and current of the signal strength of the rotation frequency band extracted and the current effective value stored in the specific frequency band storage device 60. It is compared with the peak value to determine whether or not it is a specific frequency band due to the rotation frequency. Further, based on the threshold data stored in the threshold storage device 61, it is determined whether or not it is within the normal range, that is, whether or not an abnormality has occurred (step S120).
If it is determined in step S120 that an abnormality has occurred, an alarm device (not shown) provided in the abnormality determination unit 50 or a result output of the arithmetic processing unit 10 is received, and the external output unit 15 and the display unit 13 are used. To output an alarm (step S121). Further, the alarm output may be notified to the monitoring device 200 via the communication circuit 16 as the result output of the arithmetic processing unit 10.

FIG. 5 is a diagram for explaining the effect of the first embodiment, and shows a change in the peak value of the signal intensity in the rotation frequency band when the load fluctuates. When the current load factor is a%, the peak value of the signal strength of the specific frequency band due to the rotation frequency is smaller than the peak of the sideband wave, but when the current load factor is b%, the peak value of the specific frequency band due to the rotation frequency is smaller. The peak value of the signal strength of is larger than the peak of the sideband wave. Therefore, it is difficult to detect the sideband wave, but in the present embodiment, the peak value of the signal strength of the specific frequency band is learned and stored in advance for each effective current value, so that the load fluctuates. Even if the above occurs, the peak value of the signal strength in a specific frequency band can be extracted. Further, even if the load fluctuates, the peak value of the signal intensity in the specific frequency band can be extracted for each effective current value, so that even if the load fluctuates, the sideband wave can be detected by the analysis of the current FFT.
An example in which the current load factor is a% and b% is shown, but the learning of the current load factor is performed in a certain section, for example, every 5% section of 0 to less than 5%, 5% or more and less than 10%, and so on. Good.

In the above, an example of extracting the peak value of the signal strength of the rotation frequency band and detecting the abnormality as the specific frequency band caused by the mechanical system abnormality is shown, but the specific frequency band caused by the rotor bar abnormality and the belt rotation frequency are caused. It is also possible to search for the cause of an abnormality by extracting the peak value of the signal intensity from the signal intensity of another specific frequency band such as a specific frequency band and detecting the abnormality.

As described above, according to the first embodiment, since the peak value of the signal strength of the specific frequency band is learned and stored in advance for each effective current value, an abnormality is detected with high accuracy even if the load fluctuates. Is possible. When the load fluctuates, the current effective value also fluctuates, but in the present embodiment, each current effective value has a peak value of the signal strength in a specific frequency band, and the threshold value in the normal range is also stored. , It is possible to determine the occurrence of an abnormality without being affected by load fluctuations.

Embodiment 2.
Hereinafter, the diagnostic device for the electric motor according to the second embodiment will be described with reference to the drawings.
FIG. 6 is a diagram showing a schematic configuration and an installation status of the diagnostic device for the electric motor according to the second embodiment. Similar to the first embodiment, the electric motor diagnostic device according to the second embodiment is mainly used in a control center which is a closed switchboard. In FIG. 6, the difference from FIG. 1 of the first embodiment is that the main circuit 1 is further provided with an instrument transformer 8 for detecting the voltage of the main circuit 1, and a voltage detection circuit connected to the instrument transformer 8. 9 detects the voltage of the main circuit 1, converts it into a predetermined signal, and outputs it to the arithmetic processing unit 10. Other configurations are the same as those in the first embodiment.

Next, the configuration of the arithmetic processing unit 10 will be described.
7 and 8 are diagrams showing the configuration of the arithmetic processing unit 10, FIG. 7 is a diagram for explaining a signal flow during learning using current-voltage analysis, and FIG. 8 is a diagram for diagnosing using current-voltage analysis. It is a figure for demonstrating the flow of a signal of time.
In FIGS. 7 and 8, the arithmetic processing unit 10 includes a torque conversion unit 22, a state determination unit 30, an analysis unit 40, and an abnormality determination unit 50, and is a torque and specific frequency band storage device 62 in which torque and a specific frequency are stored. , Operates in cooperation with a storage unit 11 having a threshold storage device 61 in which a threshold is stored.

First, the signal flow during learning using current-voltage analysis will be described with reference to FIG. 7.
The torque conversion unit 22 inputs a predetermined current signal converted by the current detection circuit 7 and a predetermined voltage signal converted by the voltage detection circuit 9, and the torque calculation unit 23 calculates the torque. The calculated torque value is determined by the stable state determination unit 32 of the state determination unit 30 whether or not it is in the stable state. Here, the stable state means that the torque value is constant for a certain period of time. In addition, the fixed time is a predetermined time.

When it is determined that the torque value is in a stable state, the torque of the storage unit 11 and the torque value are stored in the specific frequency band storage device 62, and the frequency analysis unit 41 of the analysis unit 40 performs FFT analysis of the current. To be told. The FFT analysis result is averaged by the averaging analysis unit 42. Noise can be reduced by this averaging process.
The sideband wave analysis unit 43 extracts sideband waves near the power supply frequency from the signal subjected to the averaging process.

Next, the specific frequency band detection unit 44 detects a specific frequency band caused by a mechanical system abnormality. The specific frequency band caused by the mechanical system abnormality detected is, for example, a specific frequency band caused by the rotation frequency (rotation frequency band), a specific frequency band caused by the rotor bar abnormality, a specific frequency band caused by the belt rotation frequency, or the like.
Then, the signal strength of the specific frequency band and the torque value when the signal strength is detected are stored in the torque of the storage unit 11 and the specific frequency band storage device 62. Here, the signal strength of a specific frequency band is stored for each torque value. That is, the normal range can be determined for each torque value.

Using the torque and the signal strength of the specific frequency band for each torque stored in the specific frequency band storage device 62, the normal range analysis unit 45 calculates the distribution of the normal range for each torque. Here, for example, the standard deviation σ is calculated by statistical processing and determined with 3σ as the threshold value. The threshold value set to be the normal range in the normal range analysis unit 45 is stored in the threshold storage device 61 of the storage unit 11.
The threshold value is not limited to 3σ, and the threshold value can be determined by statistical processing for each torque value. Alternatively, a certain torque value can be used as a reference, and a correction coefficient can be added to the reference value to obtain a threshold value. For example, the signal strength of the specific frequency band at the time of the rated torque is used as a reference, and the signal strength of the specific frequency band at the time of the torque other than the rated torque is corrected. At the time of diagnosis described later, when the rated torque is used, this stored threshold value is used as it is, but when the torque is other than the rated torque, a value obtained by adding a correction to the stored threshold value is set as the threshold value. The threshold value set to be the normal range in the normal range analysis unit 45 is stored in the threshold storage device 61 of the storage unit 11.

Next, the signal flow at the time of diagnosis using the current-voltage analysis will be described with reference to FIG.
Similar to the time of learning, the torque conversion unit 22 inputs a predetermined current signal converted by the current detection circuit 7 and a predetermined voltage signal converted by the voltage detection circuit 9, and the torque calculation unit 23 calculates the torque. .. The calculated torque value is determined by the stable state determination unit 32 of the state determination unit 30 whether or not it is in the stable state.
When it is determined that the torque value is in a stable state, the frequency analysis unit 41 of the analysis unit 40 performs FFT analysis of the current. The FFT analysis result is averaged by the averaging analysis unit 42.
The sideband wave analysis unit 43 extracts sideband waves near the power supply frequency from the signal subjected to the averaging process.

Next, the specific frequency band detection unit 44 detects a specific frequency band caused by a mechanical system abnormality. The specific frequency band caused by the mechanical system abnormality detected is, for example, a specific frequency band caused by the rotation frequency (rotation frequency band), a specific frequency band caused by the rotor bar abnormality, a specific frequency band caused by the belt rotation frequency, or the like.
The specific frequency band detected by the specific frequency band detection unit 44 is input to the abnormality determination unit 50. The abnormality determination unit 51 stores the specific frequency band and torque detected by the specific frequency band detection unit 44, and the signal strength and threshold storage device of the specific frequency band for each torque value stored in the specific frequency band storage device 62. The threshold data is input. Using these data, the abnormality determination unit 51 can determine whether or not a mechanical abnormality has occurred by comparing with high accuracy whether it is a specific frequency band caused by a mechanical abnormality by using a threshold value for each torque value. .. The determination result is output from the abnormality determination unit 50.

9A and 9B are flowcharts showing a procedure for performing a diagnosis using the diagnostic device for the electric motor according to the second embodiment. Here, the detection of the rotation frequency band as a specific frequency band will be described as an example. The diagnostic device for the electric motor according to the second embodiment shifts to a diagnostic period that can be diagnosed after a predetermined learning period.
First, the learning period will be explained.
In step S121, the current waveform and the voltage waveform are acquired. Specifically, the current detection circuit 7 connected to the instrument transformer 4 detects the load current of the main circuit 1 and converts it into a predetermined signal, and the voltage detection circuit 9 connected to the instrument transformer 8 The voltage of the main circuit 1 is detected and converted into a predetermined signal.
In step S122, the torque calculation unit 23 calculates the torque.
In step S123, the stable state determination unit 32 determines whether or not the torque value is in the stable state. If it is not in a stable state (No in step S123), the process returns to step S121. If it is in a stable state (Yes in step S123), the process proceeds to step S124, and the calculated torque value is stored in the torque and the specific frequency band storage device 62.

Next, similarly to the first embodiment, the frequency analysis unit 41 executes the current FFT analysis in step S105, and the averaging analysis unit 42 averages the analyzed current FFT results in step S106. Noise can be reduced by this averaging process.
In step S107, the sideband wave is extracted from the result of the current FFT averaged by the sideband wave analysis unit 43. In step S108, the peak of the rotation frequency band is extracted from the side band waves extracted by the specific frequency band detection unit 44, and the extracted peak value of the rotation frequency band is stored in the torque and the specific frequency band storage device 62 in step S125. Remember. The torque and the specific frequency band storage device 62 store the torque value and the peak value of the signal strength in the rotation frequency band in relation to each other (step S126).

The flow up to step S126 is the flow of the learning period. During the learning period, steps S121 to S126 are repeated (No in step S111). When the learning period is completed by repeating the process a plurality of times (Yes in step S111), the diagnosis period is reached.

In the diagnosis period, first, in step S127, the current waveform and the voltage waveform are acquired. Similar to the learning period, the current detection circuit 7 connected to the instrument transformer 4 detects the load current of the main circuit 1 and converts it into a predetermined signal, and the voltage detection circuit 9 connected to the instrument transformer 8 Detects the voltage of the main circuit 1 and converts it into a predetermined signal.
In step S128, the torque calculation unit 23 calculates the torque.
In step S129, the stable state determination unit 32 determines whether or not the torque value is in the stable state. If the torque value is not stable, the process returns to step S127 to acquire the current waveform and the voltage waveform (No in step S129). When the torque value is in a stable state, the process proceeds to step S115, and the frequency analysis unit 41 executes the current FFT analysis.

Next, in step S116, the averaging analysis unit 42 averages the analysis result of the current FFT. Noise can be reduced by this averaging process.
In step S117, the sideband wave is extracted from the analysis result of the current FFT averaged by the sideband wave analysis unit 43.
In step S118, the peak of the rotation frequency band is extracted from the side band waves extracted by the specific frequency band detection unit 44.

In step S119, the peak value and torque of the signal strength of the rotation frequency band extracted by the abnormality determination unit 51 and the peak of the signal strength of the rotation frequency band related to the torque value stored in the specific frequency band storage device 62. It is compared with the value to determine whether or not it is a specific frequency band due to the rotation frequency. Further, based on the threshold data stored in the threshold storage device 61, it is determined whether or not it is within the normal range, that is, whether or not an abnormality has occurred (step S120).
If it is determined in step S120 that an abnormality has occurred, an alarm device (not shown) provided in the abnormality determination unit 50 or a result output of the arithmetic processing unit 10 is received, and the external output unit 15 and the display unit 13 are used. To output an alarm (step S121). Further, the alarm output may be notified to the monitoring device 200 via the communication circuit 16 as the result output of the arithmetic processing unit 10.

FIG. 10 is a diagram for explaining the effect of the second embodiment, and shows a change in the peak value of the signal intensity in the rotation frequency band when the load fluctuates. When the torque value is a, the peak value of the signal strength in the specific frequency band due to the rotation frequency is smaller than the peak of the sideband wave, but when the torque value is b, the signal strength in the specific frequency band due to the rotation frequency The peak value is larger than the peak of the sideband wave. Therefore, it is difficult to detect the sideband wave, but in the present embodiment, since the peak value of the signal strength of the specific frequency band is learned and stored in advance for each torque value, the load fluctuation fluctuates. Even if it occurs, the peak value of the signal strength in a specific frequency band can be extracted. Further, even if the load fluctuates, the peak value of the signal strength in the specific frequency band can be extracted for each torque value, so that even if the load fluctuates, the sideband wave can be detected by the analysis of the current FFT.
An example in which the torque values are a and b is shown, but the learning of the torque value is performed in a certain section, for example, the torque rate (= torque value / rated torque value × 100) is 0 to less than 5%, 5% or more and less than 10%, ...・ ・ May be every 5% section.

In the above, an example of extracting the peak value of the signal strength of the rotation frequency band and detecting the abnormality as the specific frequency band caused by the mechanical system abnormality is shown, but the specific frequency band caused by the rotor bar abnormality and the belt rotation frequency are caused. It is also possible to search for the cause of an abnormality by extracting the peak value of the signal intensity from the signal intensity of another specific frequency band such as a specific frequency band and detecting the abnormality.

As described above, according to the second embodiment, since the peak value of the signal strength of the specific frequency band is learned and stored in advance for each torque value, abnormality can be detected with high accuracy even if the load fluctuates. It will be possible. When the load fluctuates, the torque value also fluctuates, but in the present embodiment, each torque value has a peak value of the signal strength in a specific frequency band, and the threshold value in the normal range is also stored. It is possible to determine the occurrence of an abnormality without receiving fluctuations in.

<Modified Example of Embodiment 2>
In the second embodiment, the torque was calculated based on the load current of the main circuit 1 detected by the current detection circuit 7 and the voltage of the main circuit 1 detected by the voltage detection circuit 9, and used for abnormality determination. However, the abnormality determination may be performed by calculating the load factor of the electric motor 5 from the torque value and learning the data of the specific frequency band corresponding to the load factor.

It is also possible to detect an abnormality in torque using the torque value calculated in the second embodiment and determine an abnormality in the electric motor due to the torque. The torque abnormality detection will be described below.

また、鎖交磁束は次の式（２）、（３）から求めることができる。

The torque Te is expressed by the following equation (1) using the stator current of the motor 5 and the interlinkage magnetic flux.

Further, the interlinkage magnetic flux can be obtained from the following equations (2) and (3).

By comparing the torque value obtained by the torque calculation unit 23 with the torque Te obtained by the equation (1), the torque abnormality can be detected.

In the above-described first and second embodiments, the electric motor diagnostic device 100 is composed of a processor 110 and a storage device 120 as shown in FIG. 11 as an example of hardware. Although the storage device is not shown, it includes a volatile storage device such as a random access memory and a non-volatile auxiliary storage device such as a flash memory. Further, an auxiliary storage device of a hard disk may be provided instead of the flash memory. The processor 110 executes the program input from the storage device 120. In this case, a program is input from the auxiliary storage device to the processor 110 via the volatile storage device. Further, the processor 110 may output data such as a calculation result to the volatile storage device of the storage device 120, or may store the data in the auxiliary storage device via the volatile storage device.

Although the present disclosure describes various exemplary embodiments and examples, the various features, embodiments, and functions described in one or more embodiments are those of a particular embodiment. It is not limited to application, but can be applied to embodiments alone or in various combinations.
Therefore, innumerable variations not illustrated are envisioned within the scope of the techniques disclosed herein. For example, it is assumed that at least one component is modified, added or omitted, and further, at least one component is extracted and combined with the components of other embodiments.

1: Main circuit, 2: Breaker for wiring, 3: Electromagnetic contactor, 4: Transformer for instrument, 5: Electric motor, 6: Mechanical equipment, 7: Current detection circuit, 8: Transformer for instrument, 9: Voltage Detection circuit, 10: Arithmetic processing unit, 11: Storage unit, 12: Setting circuit, 13: Display unit, 14: Drive circuit, 15: External output unit, 16: Communication circuit, 200: Monitoring device, 20: Current conversion unit , 21: Effective value calculation unit, 22: Torque conversion unit, 23: Torque calculation unit, 30: State determination unit, 31, 32: Stable state determination unit, 40: Analysis unit, 41: Frequency analysis unit, 42: Average Analysis unit, 43: Sideband wave analysis unit, 44: Specific frequency band detection unit, 45: Normal range analysis unit, 50: Abnormality determination unit, 51: Abnormality determination unit, 60: Current and specific frequency band storage device, 61: Threshold Storage device, 62: Torque and specific frequency band storage device, 100: Diagnostic device for electric motor

Claims (3)

A voltage detection circuit for detecting a current detection circuit and the voltage for detecting a current of the motor, the current detected voltage by the current and the voltage detection circuit which is detected by the detection circuit and processing operation for detecting an abnormality of the electric motor A diagnostic device for an electric motor including a processing unit and a storage unit for storing the calculation result of the calculation processing unit.
The arithmetic processing unit calculates a torque value from the current detected by the current detection circuit and the voltage detected by the voltage detection circuit, and determines whether the calculated torque value is in a stable state. The state determination unit, the analysis unit that FFT analyzes the current detected by the current detection circuit and extracts the peak value of the signal strength of the specific frequency band from the sideband wave, and the abnormality determination that determines whether an abnormality has occurred in the motor. Has a part and
The analysis unit stores in advance the peak value of the signal strength of the extracted specific frequency band and the torque value at that time in the storage unit, and the specific frequency band from which the threshold value for setting the normal range of the electric motor is extracted. Is set for the peak value of the signal strength of, and the threshold value is stored in the storage unit in advance.
The abnormality determination unit obtains the peak value of the signal strength of the specific frequency band obtained by FFT analysis of the current detected by the current detection circuit in the specific frequency band for each torque value stored in advance in the storage unit. An electric motor diagnostic device that determines whether or not an abnormality has occurred in the electric motor by comparing it with a peak value of signal strength and a threshold value stored in advance in the storage unit. The arithmetic processing unit acquires the torque value determined to be in a stable state by the state determination unit a plurality of times, and the peak value of the signal strength of the specific frequency band extracted by the analysis unit and the torque value at that time. The diagnostic device for an electric motor according to claim 1, wherein a plurality of the threshold values are stored in the storage unit and then the electric motor is diagnosed. The load factor of the electric motor is calculated from the torque value calculated by the torque calculation unit.
The analysis unit stores in advance the peak value of the signal strength of the extracted specific frequency band and the load factor at that time in the storage unit, and the specific frequency band from which the threshold value for setting the normal range of the electric motor is extracted. Is set for the peak value of the signal strength of, and the threshold value is stored in the storage unit in advance.
The abnormality determination unit calculates the peak value of the signal strength of the specific frequency band obtained by FFT analysis of the current detected by the current detection circuit in the specific frequency band for each load factor stored in advance in the storage unit. The diagnostic device for an electric motor according to claim 1, wherein the peak value of the signal strength and the threshold value previously stored in the storage unit are compared to determine whether or not an abnormality has occurred in the electric motor.

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