JP7596864B2 - Anomaly detection device and method - Google Patents

Description

The present invention relates to an abnormality detection device and method for detecting an abnormal state of a rotating machine such as a motor.

In monitoring equipment that diagnoses the condition of motors, when frequency analysis is performed on data obtained by a sensor, such as the motor current data, if noise components are found in addition to the peaks that always appear, this is determined to be an abnormal state, and the noise components are quantified to indicate the degree of abnormality.

For example, Patent Document 1 discloses an anomaly detection device that can identify a frequency equivalent to the period of sound emitted by rotation based on an audio signal, even when periodic noise is present. This anomaly detection device detects the envelope of an audio signal that represents periodic sounds emitted from a rotating body with a predetermined number of blades and periodic sounds emitted from other objects, calculates the frequency spectrum of the audio signal from the envelope for each frame, and detects, for each frame, a candidate frequency that corresponds to the period of sound emitted from the rotating body in that frame. Next, for each frame, the device finds the duration during which the power fluctuation of the power of the frequency spectrum components in the candidates detected for that frame is below a certain level, and identifies the candidate with the longest duration as the frequency that corresponds to the period of sound emitted from the rotating body.

JP 2019-100975 A

However, the above-mentioned conventional technology is based on the assumption that the motor rotation speed is constant, and if the motor drive frequency fluctuates during the measurement period, it becomes difficult to distinguish between the peaks that appear constantly and the noise components. As a result, there is a problem in that the peaks that appear constantly are judged to be noise components, resulting in a high degree of abnormality in the diagnosis results.

To obtain accurate diagnostic results, the user must keep the motor rotation speed constant at least while the diagnostic measurements are being made. Furthermore, if the diagnostic results indicate a significant degree of abnormality, the user must determine whether this is due to an actual motor abnormality or a change in the motor rotation speed.

The objective of the present invention is to solve the above problems and provide an abnormality detection device and method that can detect abnormal conditions in a motor with higher accuracy than conventional techniques.

An abnormality detection device according to one aspect of the present invention comprises:
1. An abnormality detection device that detects whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power source,
performing a frequency analysis on the current or voltage data to calculate a frequency width of a peak having a maximum peak value of the frequency analysis result;
a signal processing unit that determines that the rotation speed of the motor has changed when the calculated frequency width is equal to or greater than a predetermined frequency width threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than the predetermined frequency width threshold;
Prepare.

An abnormality detection device according to another aspect of the present invention includes:
1. An abnormality detection device that detects whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power source,
a time period of the current or voltage data is divided into a plurality of periods and frequency-analyzed to calculate a difference frequency between a maximum frequency and a minimum frequency among the frequencies having maximum peak values in each frequency analysis result;
a signal processing unit that determines that the rotation speed of the motor has changed when the calculated difference frequency is equal to or greater than a predetermined difference frequency threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than a predetermined frequency width threshold;
Prepare.

Therefore, the abnormality detection device according to the present invention can detect an abnormal state of the motor with higher accuracy than the conventional technology by detecting whether the rotation speed of the motor has changed.

1 is a block diagram showing an example of the configuration of a motor abnormality detection device 4 according to a first embodiment. FIG. 2 is a flowchart showing an abnormality detection process executed by the processor 10 of FIG. 1 . 2 is a graph showing an example of a spectrum of a motor current calculated by the abnormality detection device 4 of FIG. 1 . 3 is a graph showing an example of a spectrum of a motor current for explaining a concept of setting a frequency width threshold value in the abnormality detection process of FIG. 2 . 10 is a flowchart showing an abnormality detection process executed by a processor 10 of an abnormality detection device 4 according to a second embodiment. 10 is a graph showing an example of a spectrum of a motor current calculated by an abnormality detection device 4 according to a second embodiment. 11 is a flowchart showing an abnormality detection process executed by a processor 10 of an abnormality detection device 4 according to a third embodiment. 13 is a graph showing an example of a spectrum of a motor current calculated by an abnormality detection device 4 according to a third embodiment. 13 is a flowchart showing an abnormality detection process executed by a processor 10 of an abnormality detection device 4 according to a fourth embodiment. 13 is a graph showing an example of a spectrum of a motor current calculated by an abnormality detection device 4 according to a fourth embodiment.

Embodiments of the present invention will be described below with reference to the drawings. Note that identical or similar components are given the same reference numerals.

(Embodiment 1)
Fig. 1 is a block diagram showing an example of the configuration of a motor abnormality detection device 4 according to embodiment 1. Fig. 1 shows the abnormality detection device 4 and its peripheral circuits, in which AC power from an AC power source 1 is supplied to a motor 2 via a current sensor 3. In Fig. 1, the abnormality detection device 4 is configured to include an AD converter 11, a processor 10 having a frequency analysis unit 12 and an abnormality detection unit 13 to configure a "signal processing unit", and a display unit 14. Here, the frequency analysis unit 12 and the abnormality detection unit 13 have memories 12m and 13m, respectively.

In FIG. 1, the current sensor 3 detects the current value of the current supplied from the AC power source 1 to the motor 2, and outputs a detection signal indicating the current value to the AD converter 11. The AD converter 11 converts the input detection signal into current data indicating the current value, and then outputs the signal to the frequency analysis unit 12. The frequency analysis unit 12 performs a fast Fourier transform (FFT) process on the AD converted value of the current data for a predetermined time period T1, calculates the FFT value (power) of the spectrum, and stores it in the memory 12m. The abnormality detection unit 13 searches for the frequency width (e.g., half-width) of the maximum peak value in the calculated FFT value of the spectrum, stores it in the memory 13m, and determines whether the frequency width of the searched maximum peak value is equal to or greater than a predetermined frequency width threshold value. When the frequency width threshold value is equal to or greater than the frequency width threshold value, it determines that the drive frequency has fluctuated and that the rotation speed of the motor 1 has changed, and displays the determination result on the display unit 14. On the other hand, when the frequency width of the searched maximum peak value does not exceed a predetermined frequency width threshold, the abnormality detection unit 13 determines that the drive frequency is stable and that the rotation speed of the motor 1 is not changing, and displays the determination result on the display unit 14.

Figure 2 is a flowchart showing the anomaly detection process executed by the processor 10 in Figure 1.

2, the AD conversion value of the current value for a predetermined time period T1 detected by the current sensor 3 is input, and in step S2, the frequency analysis unit 12 performs FFT processing on the AD conversion value of the current value for the predetermined time period T1 using the memory 12m to calculate the spectrum of the FFT value (power) with respect to the spectrum. Next, in step S3, the abnormality detection unit 13 calculates the frequency width (e.g., half-width) of the maximum peak value in the FFT value of the calculated spectrum, and in step S4, determines whether the calculated frequency width is equal to or greater than a predetermined frequency width threshold value. The abnormality detection unit 13 executes the processing of steps S3 to S6. In step S4, if YES, the process proceeds to step S5, whereas if NO, the process proceeds to step S6. In step S5, it is determined that the drive frequency has fluctuated and the rotation speed of the motor 1 has changed, and the determination result is displayed on the display unit 14, and the abnormality detection process is terminated. On the other hand, in step S6, it is determined that the drive frequency is stable and the rotation speed of the motor 1 has not changed, and the determination result is displayed on the display unit 14, and the abnormality detection process is terminated.

Next, an example of the anomaly detection device 4 according to embodiment 1 will be described below.

Figure 3 is a graph showing an example of the spectrum of the motor current calculated by the abnormality detection device 4 of Figure 1.

In the first embodiment, as shown in FIG. 3, if the frequency width (e.g., half-value width Fwp) of the maximum peak value obtained by frequency analysis of the drive current of the motor 1 exceeds a preset frequency width threshold value, it is determined that the drive frequency has fluctuated. For example, a current waveform over a period of 4 seconds is frequency analyzed, and for example, the half-value width (width of a drop of -6 dB) Fwp is calculated for the maximum peak, and it is determined that the drive frequency has fluctuated when the calculated half-value width exceeds a predetermined frequency width threshold value.

Figure 4 is a graph showing an example of a motor current spectrum to explain the concept of setting the frequency width threshold in the anomaly detection process of Figure 2.

The frequency width threshold is preferably set to approximately twice the slip frequency of motor 1 (usually around 1 Hz to 2 Hz). This is because motor rotor abnormalities tend to appear in the frequency band of drive frequency ± slip frequency, so if the drive frequency fluctuates by the slip frequency, the rotor abnormality component will be buried in the drive frequency component and the abnormality will not be detected. In order to determine whether the abnormality can be detected, it is preferable to set the frequency width threshold based on the above idea.

As described above, according to the first embodiment, it is determined whether the frequency width of the peak having the maximum peak value of the spectrum calculated by performing FFT processing on the current data is equal to or greater than a predetermined frequency width threshold, and if it is equal to or greater than the frequency width threshold, it is determined whether the rotation speed of the motor 1 has changed depending on whether the drive frequency is fluctuating. Therefore, it is possible to detect an abnormal state of the motor with higher accuracy than the conventional technology.

In the above embodiment 1, the frequency width determined in step S4 in FIG. 4 is, for example, a half-width, but the present invention is not limited to this, and may be a predetermined frequency width related to the peak of the maximum peak value. In other words, it may be a frequency width that is a predetermined value (e.g., -3 dB, -8 dB, etc.) below the maximum peak value. This is the same in embodiments 2 to 4 described below.

(Embodiment 2)
Fig. 5 is a flowchart showing an abnormality detection process executed by the processor 10 of the abnormality detection device 4 according to the second embodiment. The device configuration of the abnormality detection device 4 according to the second embodiment is the same as that shown in Fig. 1. The abnormality detection process according to the second embodiment has the following differences compared to the abnormality detection process according to the first embodiment shown in Fig. 2.

(1) Instead of the process of step S5, the process of step S5A is executed.
(2) Steps S7 to S11 are added and executed.
The differences will be explained below.

The frequency width threshold in step S4 is the same as that in embodiment 1, but the frequency width threshold in step S9 is a different frequency width threshold from that in embodiment 1. This also applies to embodiment 3, which will be described later.

In FIG. 5, in step S5A, the abnormality detection unit 13 determines that the drive frequency is fluctuating, and then proceeds to step S7. The abnormality detection unit 13 executes the processes of steps S3 to S11. In step S7, the AD conversion value of the current value of the predetermined period T1 is divided into a plurality of predetermined time periods T2 (<T1), and for each time period T2, FFT processing is performed using the memory 12m to calculate the FFT value (power). Next, in step S8, based on the FFT value of each time period T2, the maximum peak value of each time period T2 is searched for, and the half-width of the maximum peak value is calculated. In step S9, it is determined whether the calculated frequency width is equal to or greater than the predetermined another frequency width threshold value. Here, the another frequency width threshold value is set to a value smaller than the frequency width threshold value of step S4. For example, if the number of divisions of the time period of step S7 is Nd, the another frequency width threshold value is set to a value obtained by dividing the frequency width threshold value of step S4 by Nd.

In step S9, if the answer is YES, the process proceeds to step S10, whereas if the answer is NO, the process proceeds to step S11. In step S10, it is determined that the drive frequency is fluctuating, and that the rotation speed of motor 1 has changed, and the determination result is displayed on display unit 14, and the abnormality detection process is terminated. On the other hand, in step S11, it is determined that the drive frequency is stable, that the rotation speed of motor 1 has not changed, and also that the frequency of the maximum peak value is the drive frequency, and the determination result is displayed on display unit 14, and the abnormality detection process is terminated.

Figure 6 is a graph showing an example of the spectrum of the motor current calculated by the abnormality detection device 4 according to the second embodiment.

In Fig. 6, if the frequency width (half-value width Fpw) of the maximum peak value of the spectrum obtained by frequency analysis of the motor current data is equal to or greater than a predetermined frequency width threshold value, it is determined that the drive frequency is fluctuating (Fig. 6(a)). Next, the time period T1 for frequency analysis is divided into smaller parts and frequency analysis is performed again. In the example of Fig. 6, as shown in Figs. 6(b) to 6(e),
(1) a time period between 0 and 1 second; and
(2) a time period of 1 to 2 seconds; and
(3) a time period of 2 to 3 seconds; and
(4) a time period of 3 to 4 seconds; and
Here, if the half-width Fpw of the maximum peak value obtained by the frequency analysis after dividing is equal to or greater than a predetermined frequency width threshold, it is determined that the drive frequency has fluctuated, and if it is less than the predetermined frequency width threshold, the frequency corresponding to the maximum peak value is determined to be the drive frequency.

As described above, according to the second embodiment, the frequency width of the peak having the maximum peak value of the spectrum calculated by performing FFT processing on the current data is determined to be equal to or greater than a predetermined frequency width threshold, and if it is equal to or greater than the frequency width threshold, it is determined that the drive frequency is fluctuating. After that, the time period is divided and FFT processing is performed again to determine the fluctuation in the drive frequency using another frequency width threshold, and it is determined that the rotation speed of the motor 1 has changed. Therefore, it is possible to detect an abnormal state of the motor with higher accuracy than the conventional technology.

(Embodiment 3)
Fig. 7 is a flowchart showing an abnormality detection process executed by the processor 10 of the abnormality detection device 4 according to the third embodiment. The device configuration of the abnormality detection device 4 according to the third embodiment is the same as that shown in Fig. 1. The abnormality detection process according to the third embodiment has the following differences compared to the abnormality detection process according to the second embodiment shown in Fig. 5.

(1) Instead of step S6, the process of step S6A is executed.
(2) Instead of step S7, the process of step S7A is executed.
(3) Steps S21 and S22 are executed instead of steps S10 and S11, and if the answer is YES in step S9, the time periods T1 and T2 in step S21 are changed, and then the process returns to step S7A.
The differences will be explained below.

If the answer is NO in step S6 in FIG. 7, then in step S6A it is determined that the drive frequency is stable and the rotation speed of the motor 1 is not changing, and the frequency of the maximum peak value is determined to be the drive frequency, and the determination result is displayed on the display unit 14, and the abnormality detection process is terminated. On the other hand, if the answer is YES in step S6, then in step S5A it is determined that the drive frequency is fluctuating, and the process proceeds to step S7A.

In step S7A, the AD conversion value of the current value for a predetermined time period T1 is divided into two predetermined time periods T2, and for each time period T2, FFT processing is performed using memory 12m to calculate the FFT value (power) of the spectrum. In step S8, the maximum peak value for each time period T2 is searched for based on the FFT value for each time period T2, and the frequency width (e.g., half-width) of the maximum peak value is calculated. In step S9, it is determined whether the calculated frequency width is greater than or equal to the predetermined frequency width threshold value, and if YES, the process proceeds to step S21, whereas if NO, the process proceeds to step S22.

In step S22, it is determined that the drive frequency of motor 1 is stable and the rotation speed of motor 1 is not changing, and the frequency of the maximum peak value is determined to be the drive frequency. The determination result is displayed on display unit 14 and the abnormality detection process is terminated. In step S21, it is determined that the drive frequency is fluctuating, time period T1/2 (which corresponds to time period T2 at the current point in time) is substituted for time period T1, time period T2/2 is substituted for time period T2, and the process returns to step S7A. This means that the time period targeted by the FFT process to calculate the frequency width is set to half that of the previous calculation.

In the above third embodiment, the other frequency width threshold in step S9 is set to, for example, half the frequency width threshold in step S3, but the present invention is not limited to this, and when judging the processing of step S9 for the second or subsequent times, the other frequency width threshold may be further halved. That is, in step S21, the other frequency width threshold may be halved and replaced. In this case, the time period T1=2×T2, and when the time T1=Nf×T2 (N≧3, a natural number), the other frequency width threshold may be replaced by 1/Nf.

Figure 8 is a graph showing an example of the spectrum of the motor current calculated by the abnormality detection device 4 according to the third embodiment.

In embodiment 3, when the frequency width of the maximum peak value obtained by frequency analysis of the current data of motor 1 is equal to or greater than a predetermined frequency width threshold, it is determined that the drive frequency is fluctuating, and in this case, the time period for frequency analysis is divided in half and frequency analysis is performed again. When the frequency width of the maximum peak value obtained by this division and frequency analysis is equal to or greater than the other frequency width threshold, it is determined that the drive frequency is fluctuating, but when it is less than the frequency width threshold, the frequency corresponding to the maximum peak value is determined to be the drive frequency. On the other hand, when the frequency width of the maximum peak value obtained by division and frequency analysis is equal to or greater than the other frequency width threshold, the time period is further halved and frequency analysis is performed, and the same determination is repeated.

As shown in FIG. 8, for example, current data for 4 seconds is frequency analyzed, and a half-width Fpw, for example half-value (-6 dB) below the maximum peak value, is calculated (FIG. 8(a)). When the half-width Fpw is equal to or greater than a predetermined frequency width threshold, it is determined that the drive frequency is fluctuating (FIG. 8(b)). When the half-width Fpw is less than the frequency width threshold, the frequency corresponding to the peak having the maximum peak value is determined to be the drive frequency (FIG. 8(c)). On the other hand, if it is determined that the drive frequency is fluctuating, frequency analysis is performed again, for example, every 2 seconds (FIGS. 8(d) and (e)). A half-width Fpw, for example half-value below the maximum peak value of the spectrum, which is the frequency analysis result every 2 seconds, is calculated, and when the recalculated half-width Fpw is less than another predetermined frequency width threshold, the peak related to the half-width Fpw is determined to be the peak of the drive frequency. Here, when the half-width Fpw is equal to or greater than the other frequency width threshold, it is determined that the drive frequency is fluctuating, and frequency analysis is further performed every second, repeating this process until the maximum peak of the drive frequency at which the drive frequency is fluctuating can be determined.

As described above, according to the third embodiment, it is determined whether the frequency width of the peak having the maximum peak value of the spectrum calculated by performing FFT processing on the current data is equal to or greater than a predetermined frequency width threshold, and if it is equal to or greater than the frequency width threshold, it is determined that the drive frequency is fluctuating. Next, the time period is divided into two and FFT processing is performed again to determine the fluctuation of the drive frequency using another frequency width threshold, and if it is determined that the drive frequency is still fluctuating, the time period is further divided into two and FFT processing is performed again to determine the fluctuation of the drive frequency using another frequency width threshold, thereby detecting whether the rotation speed of the motor 1 has changed. Therefore, it is possible to detect an abnormal state of the motor with higher accuracy than with conventional technology.

(Embodiment 4)
Fig. 9 is a flowchart showing an abnormality detection process executed by the processor 10 of the abnormality detection device 4 according to the fourth embodiment. The device configuration of the abnormality detection device 4 according to the fourth embodiment is the same as that shown in Fig. 1. The abnormality detection process according to the third embodiment has the following differences compared to the abnormality detection process according to the first embodiment shown in Fig. 2.

(1) The processes of steps S2, S3, and S4 are replaced with steps S2A, S3A, and S4A, respectively.
The differences will be explained below.

In step S2A of FIG. 9, the frequency analysis unit 12 divides the AD conversion value of the current value in the predetermined period T1 into a plurality of predetermined time periods T2 (<T1) of Nda, and performs FFT processing using the memory 12m for each time period T2 to calculate the FFT value (power). Here, Nda is a natural number of 2 or more. Next, in step S3A, the abnormality detection unit 13 searches for the frequency of the maximum peak value of each time period T2 based on the FFT value of each time period T2, and calculates the difference frequency between the highest frequency and the lowest frequency among the plurality of Nda frequencies found. In step S4A, it is determined whether the calculated difference frequency is greater than or equal to the predetermined difference frequency threshold value, and if YES, the process proceeds to step S5, while if NO, the process proceeds to step S6. In step S5, it is determined that the drive frequency has fluctuated and the rotation speed of the motor 1 has changed, and the determination result is displayed on the display unit 14 to end the abnormality detection process. On the other hand, in step S6, it is determined that the drive frequency is stable and the rotation speed of the motor 1 has not changed, and the result of this determination is displayed on the display unit 14.

Figure 10 is a graph showing an example of the spectrum of the motor current calculated by the abnormality detection device 4 according to the fourth embodiment.

In the fourth embodiment, measures to be taken when the drive frequency of the motor 1 fluctuates discontinuously are described. In the fourth embodiment, as shown in FIG. 10, the time period for frequency analysis is divided and frequency analysis is performed. The frequency corresponding to the maximum peak value in the spectrum of each frequency analysis result is obtained. If the difference between the maximum and minimum values of each frequency is equal to or greater than the difference frequency threshold, it is determined that the drive frequency of the motor 1 is fluctuating.

Specifically, for example, a four-second current waveform is divided into one-second intervals and frequency analysis is performed, and if the difference between the highest and lowest frequencies corresponding to each maximum peak value is equal to or greater than the difference frequency threshold, it is determined that the drive frequency is fluctuating. In the example of Figure 10, if the highest frequency is 52 Hz, the lowest frequency is 49 Hz, and the difference frequency threshold is 2 Hz, then 52 - 49 = 3 Hz, which is equal to or greater than 2 Hz, and it is determined that the drive frequency of motor 1 is fluctuating.

As described above, according to the fourth embodiment, the time period to be frequency analyzed is divided and frequency analysis is performed, the frequency corresponding to the maximum peak value in the spectrum of each frequency analysis result is obtained, and if the difference between the maximum and minimum values of each frequency, i.e., the difference between the maximum frequency and the minimum frequency, is equal to or greater than the difference frequency threshold value, it is determined that the drive frequency of motor 1 has fluctuated and the rotation speed of motor 1 has changed. This makes it possible to detect an abnormal state of the motor with higher accuracy than conventional techniques.

(Modification)
In each of the above embodiments, current data of the current supplied from the AC power supply to the motor is subjected to frequency analysis to determine whether or not the rotation speed of the motor has changed. However, the present invention is not limited to this, and it is also possible to similarly determine whether or not the rotation speed of the motor has changed by performing frequency analysis on voltage data of the voltage supplied from the AC power supply to the motor.

As described above in detail, according to the present invention, for example, the motor current data is frequency-analyzed, and the frequency width of the maximum peak value of the spectrum of the frequency analysis result is equal to or greater than a predetermined frequency width threshold value, or the motor current data is divided into a plurality of time periods and frequency-analyzed, and the difference between the highest and lowest frequencies among the frequencies of the maximum peak values of the spectrum of each frequency analysis result is equal to or greater than a predetermined difference frequency threshold value, thereby determining whether the motor drive frequency has fluctuated, thereby determining whether the motor rotation speed has changed. This makes it possible to detect an abnormal state of the motor with higher accuracy than conventional technology.

1 AC power supply 2 Motor 3 Current sensor 4 Abnormality detection device 10 Processor (signal processing unit)
11 AD converter 12 Frequency analysis unit 12m Memory 13 Abnormality detection unit 13m Memory 14 Display unit

Claims (12)

1. An abnormality detection device that detects whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power source,
performing a frequency analysis on the current or voltage data to calculate a frequency width of a peak having a maximum peak value of the frequency analysis result;
a signal processing unit that determines that the rotation speed of the motor has changed when the calculated frequency width is equal to or greater than a predetermined frequency width threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than the predetermined frequency width threshold;
Equipped with an abnormality detection device. The frequency width is a half-width of a peak having the maximum peak value.
The abnormality detection device according to claim 1 . When the calculated frequency width is equal to or greater than a predetermined frequency width threshold, the signal processing unit:
Dividing a time period of the current or voltage data into a plurality of time periods and performing frequency analysis for each of the time periods, thereby calculating a frequency width of a maximum peak value among the results of each of the frequency analysis;
determining that the rotation speed of the motor has changed when the calculated frequency width is equal to or greater than another predetermined frequency width threshold, and determining that the rotation speed of the motor has not changed when the calculated frequency width is less than another predetermined frequency width threshold;
The abnormality detection device according to claim 1 or 2. When the calculated frequency width is equal to or greater than a predetermined frequency width threshold, the signal processing unit:
Dividing a time period of the current or voltage data into two time periods and performing a frequency analysis on each of the two time periods, thereby calculating a frequency width of a maximum peak value among the results of each of the frequency analysis;
determining that the rotation speed of the motor is not changing when the calculated frequency width is less than another predetermined frequency width threshold value;
The abnormality detection device according to claim 1 or 2. when the calculated frequency width is equal to or greater than another predetermined frequency width threshold value, the signal processing unit further divides the two divided time periods of the current or voltage data into two time periods and performs frequency analysis on each of the two time periods to calculate a frequency width of a maximum peak value among the frequency analysis results;
determining that the rotational speed of the motor is not changing when the calculated frequency width is less than a further predetermined frequency width threshold value;
The abnormality detection device according to claim 4. 1. An abnormality detection device that detects whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power source,
a time period of the current or voltage data is divided into a plurality of periods and frequency-analyzed to calculate a difference frequency between a maximum frequency and a minimum frequency among the frequencies having maximum peak values in each frequency analysis result;
a signal processing unit that determines that the rotation speed of the motor has changed when the calculated difference frequency is equal to or greater than a predetermined difference frequency threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than a predetermined frequency width threshold;
Equipped with an abnormality detection device. 1. An abnormality detection method for detecting whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power supply, comprising:
A signal processing unit performs frequency analysis on the current or voltage data to calculate a frequency width of a peak having a maximum peak value of the frequency analysis result;
a step in which the signal processing unit determines that the rotation speed of the motor has changed when the calculated frequency width is equal to or greater than a predetermined frequency width threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than the predetermined frequency width threshold;
Anomaly detection methods. The frequency width is a half-width of a peak having the maximum peak value.
The anomaly detection method according to claim 7. When the calculated frequency width is equal to or greater than a predetermined frequency width threshold,
A step of dividing a time period of the current or voltage data into a plurality of time periods and performing frequency analysis for each of the time periods, thereby calculating a frequency width of a maximum peak value among the results of the frequency analysis;
a step in which the signal processing unit determines that the rotation speed of the motor has changed when the calculated frequency width is equal to or greater than another predetermined frequency width threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than another predetermined frequency width threshold;
The anomaly detection method according to claim 7 or 8, further comprising: When the calculated frequency width is equal to or greater than a predetermined frequency width threshold,
A step of dividing a time period of the current or voltage data into two time periods and performing a frequency analysis on each of the two time periods, thereby calculating a frequency width of a maximum peak value among the results of the frequency analysis;
and a step in which the signal processing unit determines that the rotation speed of the motor is not changing when the calculated frequency width is less than another predetermined frequency width threshold value.
The anomaly detection method according to claim 7 or 8, further comprising: a step of, when the calculated frequency width is equal to or larger than another predetermined frequency width threshold value, the signal processing unit further divides the two divided time periods of the current or voltage data into two time periods and performs frequency analysis on each of the two time periods, thereby calculating a frequency width of a maximum peak value among the frequency analysis results;
The signal processing unit determines that the rotation speed of the motor is not changing when the calculated frequency width is less than a predetermined further frequency width threshold value;
The method of claim 10 further comprising: 1. An abnormality detection method for detecting whether a rotation speed of a motor has changed based on a current or voltage supplied to the motor from a power supply, comprising:
A signal processing unit divides a time period of the current or voltage data into a plurality of periods and performs frequency analysis to calculate a difference frequency between a maximum frequency and a minimum frequency among the frequencies having maximum peak values in each frequency analysis result;
a step in which the signal processing unit determines that the rotation speed of the motor has changed when the calculated difference frequency is equal to or greater than a predetermined difference frequency threshold, and determines that the rotation speed of the motor has not changed when the calculated frequency width is less than a predetermined frequency width threshold;
Anomaly detection methods.

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