Anomaly detection device and method

Abstract

To detect an abnormal state of a motor with higher accuracy compared with the conventional art.SOLUTION: A device for detecting an abnormality comprises a signal processing unit that detects an abnormal state of a motor on the basis of a current or a voltage supplied from a power supply to the motor. The signal processing unit performs frequency analysis of data of the current or the voltage to calculate a predetermined frequency width with reference to a reference drive frequency related to the maximum peak value of frequency analysis results. When the calculated frequency width is equal to or more than a predetermined threshold, the signal processing unit determines that a rotational speed of the motor changes, and when the calculated frequency width is less than the predetermined threshold, determines that there is no change in the rotational speed of the motor, thereby detecting the abnormal state of the motor. Here, the frequency width is a difference between a pair of frequencies corresponding to a pair of values decreased by a predetermined value from the maximum peak value, such as a half value width that is a difference between a pair of frequencies corresponding to a pair of values decreased by a half of the value from the maximum peak value.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an abnormality detection device and method for detecting an abnormal state of a rotating machine such as a motor. [Background technology]

[0002] In monitoring equipment that diagnoses the condition of a motor, when data obtained by a sensor, for example, is frequency-analyzed, if noise components are found in addition to the peaks that always appear, this is determined to be an abnormal condition, and the noise components are quantified to indicate the degree of abnormality.

[0003] For example, Patent Document 1 discloses an abnormality detection device capable of identifying a frequency corresponding to the period of a sound emitted by rotation based on an audio signal even in the presence of periodic noise. This abnormality detection device detects the envelope of an audio signal representing a periodic sound emitted from a rotating body having a predetermined number of blades and a periodic sound emitted from other objects, calculates a frequency spectrum of the audio signal from the envelope for each frame, and detects a candidate frequency corresponding to the period of the sound emitted from the rotating body in each frame. Next, for each frame, the device finds a duration during which the power fluctuation of the power of the frequency spectrum components in the candidate detected for that frame is equal to or less than a certain value, and identifies the candidate with the longest duration as the frequency corresponding to the period of the sound emitted from the rotating body. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2019-100975 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned conventional technology is based on the premise that the rotation speed of the motor is constant, and if the drive frequency of the motor fluctuates during the measurement period, it becomes difficult to distinguish between the peaks that always appear and the noise components. As a result, there is a problem that the peaks that always appear are judged as noise components, and the degree of abnormality in the diagnosis result becomes high.

[0006] In order to obtain accurate diagnostic results, the user must keep the motor rotation speed constant at least while performing diagnostic measurements. Also, if the diagnostic results indicate a large degree of abnormality, the user must determine whether this is due to an actual motor abnormality or a change in the motor rotation speed.

[0007] SUMMARY OF THE PRESENT EMBODIMENT An object of the present invention is to solve the above problems and provide an abnormality detection device and method capable of detecting an abnormal state of a motor with higher accuracy than the prior art. [Means for solving the problem]

[0008] An abnormality detection device according to one aspect of the present invention comprises: An abnormality detection device including a signal processing unit that detects an abnormal state of a motor based on a current or voltage supplied to the motor from a power source, The signal processing unit includes: performing frequency analysis on the current or voltage data to calculate a predetermined frequency width based on a reference drive frequency associated with a maximum peak value of the frequency analysis result; When the calculated frequency width is equal to or greater than a predetermined threshold value, it is determined that the rotational speed of the motor has changed, whereas when the calculated frequency is less than a predetermined threshold value, it is determined that the rotational speed of the motor has not changed, thereby detecting an abnormal state of the motor. Effect of the Invention

[0009] Therefore, with the abnormality detection device etc. of the present invention, by performing frequency analysis on the current or voltage data, calculating a predetermined frequency width based on a reference drive frequency related to the maximum peak value of the frequency analysis result, and determining whether or not the rotation speed of the motor has changed based on the calculated frequency width, it is possible to detect an abnormal state of the motor with greater accuracy than with conventional technology. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram showing an example of the configuration of a motor abnormality detection device 4 according to an embodiment of the present invention. [Diagram 2] 4 is a flowchart showing an abnormality detection process executed by the processor 10 of FIG. [Figure 3A] 3 is a flowchart showing an abnormality diagnosis process which is a subroutine of the abnormality detection process in FIG. 2. [Figure 3B] 3 is a flowchart showing an abnormality diagnosis process according to a modified example, which is a subroutine of the abnormality detection process in FIG. 2. [Figure 4] 2 is a graph showing an example of a spectrum of a motor current calculated by the abnormality detection device 4 of FIG. 1. [Figure 5A] 1 is a graph showing an example of a spectrum of a drive current in a motor in which a load imbalance occurs. [Figure 5B] 5B is a graph showing the spectrum of the drive current after removing the DC component and harmonics from the FFT waveform of FIG. 5A. [Figure 6] 1 is a graph showing the spectrum of the load current when the motor is normal and when an abnormality occurs due to imbalance. [Figure 7] 1 is a graph showing the spectrum of the load current when the motor is normal and when an abnormality due to cavitation occurs. [Figure 8] 4 is a graph showing the spectrum of the load current when the motor is normal and when an abnormality occurs due to bearing deterioration. [Figure 9]It is a graph showing the spectrum of the drive current in a state where noise due to the influence of inverter drive and minute noise generated by other factors are present in the signal to be detected. [Figure 10] It is a graph showing the spectra of the load current when the motor is normal and when an abnormality due to unbalance occurs.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments according to the present invention will be described with reference to the drawings. The same or similar components are denoted by the same reference numerals.

[0012] (Embodiment) FIG. 1 is a block diagram showing a configuration example of a motor abnormality detection device 4 according to an embodiment. FIG. 1 shows the abnormality detection device 4 and its peripheral circuits, and AC power from an AC power supply 1 is supplied to a motor 2 via a current sensor 3. In FIG. 1, the abnormality detection device 4 includes an AD converter 11, a processor 10 that has a frequency analysis unit 12 and an abnormality detection unit 13 to constitute a “signal processing unit”, and a display unit 14. Here, the abnormality detection unit 13 has a degradation degree calculation unit 13a, and the frequency analysis unit 12 and the abnormality detection unit 13 each have memories 12m and 13m.

[0013] In FIG. 1, the current sensor 3 detects the current value of the drive current supplied from the AC power supply 1 to the motor 2, and outputs a detection signal indicating the current value to the AD converter 11. The AD converter 11 AD-converts the input detection signal into current data indicating the current value, and then outputs it to the frequency analysis unit 12. The frequency analysis unit 12 stores the input current data in the memory 12m for each predetermined frame period T2 (<T1) over a predetermined time period T1, and then executes fast Fourier transform (FFT) processing on the current data for each frame period T2, and searches for the maximum peak value of each frame period T2. The frequency analysis unit 12 calculates the average value of the plurality of searched maximum peak values by applying a predetermined removal condition described later, and stores the frequency corresponding to the calculated average value of the maximum peak values in the memory 13m as the drive frequency fd.

[0014] Next, the abnormality detection unit 13 calculates the change amount Δfd of the drive frequency fd from the predetermined reference drive frequency fr (for example, the half-value width, which refers to the frequency width at which the current value drops by 6 dB from the maximum value), determines whether it is equal to or greater than a predetermined threshold value Δfth, and when it is equal to or greater than the threshold value Δfth, determines that the rotational speed of the motor 1 has changed and displays the determination result on the display unit 14. On the other hand, when the change amount Δfd (frequency width) of the drive frequency fd is less than the predetermined threshold value Δfth, the abnormality detection unit 13 determines that the rotational speed of the motor 1 has not changed, and further executes an abnormality diagnosis process (details will be described later) for determining the degree of deterioration of the motor 2.

[0015] FIG. 2 is a flowchart showing the abnormality detection process executed by the processor 10 of FIG. 1.

[0016] In step S1 of FIG. 2, the current data of the AD conversion value for the predetermined time period T1 detected by the current sensor 2 is input. In step S2, the frequency analysis unit 12 divides the current data of the AD conversion value for the predetermined time period T1 into a plurality of predetermined frame periods T2 (<T1), and for each frame period T2, executes FFT processing using the memory 12m to calculate the FFT value (power) of the spectrum. Next, in step S3, the frequency analysis unit 12 searches for the maximum peak value of each frame period T2 based on the FFT value of each frame period T2. Then, in step S4, the average value of the plurality of searched maximum peak values is calculated with or without applying a predetermined removal condition.

[0017] Furthermore, in step S5, the abnormality detection unit 13 stores the frequency corresponding to the average value of the calculated maximum peak values ​​in the memory 13m as the reference drive frequency fr, and then in step S6, calculates the change amount Δfd of the drive frequency fd from the reference drive frequency fr (for example, the difference between a pair of frequencies corresponding to a pair of FFT values ​​that are half-value lower than the maximum peak value related to the reference drive frequency fr, i.e., the half-value frequency width or the half-value width). Then, in step S7, it is determined whether Δfd≧Δfth (where Δfth is a threshold value), and if YES, the process proceeds to step S8, whereas if NO, the process proceeds to step S9. In step S8, it is determined that the rotation speed of the motor 1 has changed, the determination result is displayed on the display unit 14, and the abnormality detection process is terminated. In step S9, it is determined that the rotation speed of the motor 1 has not changed, and an abnormality diagnosis process (FIG. 4A or FIG. 4B) is executed to diagnose the deterioration of the motor 1.

[0018] Fig. 3A is a flowchart showing an abnormality diagnosis process which is a subroutine of the abnormality detection process of Fig. 2. In Fig. 3A, the deterioration level of the motor 2 is calculated by a deterioration level calculation unit 13a, as will be described in detail later.

[0019] 3A, fundamental and harmonic components are removed from the current data, and the current data is summed to calculate the degree of deterioration, which will be described later. In step S12, it is determined whether the degree of deterioration is greater than or equal to a deterioration threshold value. If the result is YES, the process proceeds to step S13, whereas if the result is NO, the process proceeds to step S14. In step S13, it is determined that the motor has deteriorated, and the result of the determination is displayed on display unit 14, and the abnormality detection process is terminated. In step S14, it is determined that the motor has not deteriorated, and the result of the determination is displayed on display unit 14.

[0020] Fig. 3B is a flowchart showing an abnormality diagnosis process according to a modified example, which is a subroutine of the abnormality detection process in Fig. 2. The abnormality diagnosis process in Fig. 3B differs from the abnormality diagnosis process in Fig. 3A in that step S11A is executed instead of step S11. Here, in step S11A, fundamental wave components and harmonic components below a predetermined level are cut from the current data, and the current data are added up to calculate the deterioration degree described below.

[0021] (Abnormality detection processing) Fig. 4 is a graph showing an example of the spectrum of the motor current calculated by the anomaly detection device 4 of Fig. 1. Here, the current data of the motor current acquired over a predetermined time period is frequency analyzed by dividing a predetermined frame period and executing FFT processing, and peak values ​​distributed within a preset width (e.g., half-width, which means a frequency width where the current value is 6 dB lower than the maximum value) are extracted from the maximum peak values ​​for each divided frame period, and the average value is set as the reference drive frequency fr.

[0022] For example, a 4-second waveform of current data is divided into 0.5-second frame periods and frequency analyzed, and the average of the frequencies whose maximum peaks fall within ±0.2 Hz is set as the reference driving frequency fr. In the example of Table 1 below, the frequencies that fall within ±0.2 Hz are 60.1 Hz, 60 Hz, 59.9 Hz, 60.0 Hz, and 60.2 Hz, so the average of these five values, 60.04 Hz, is set as the reference driving frequency fr.

[0023] [Table 1] Reference drive frequency fr ------------------------------------------------------------------ FFT for current data from 0.0 to 0.5 seconds: 60.1Hz FFT for current data from 0.5 to 1.0 seconds: 60.0Hz FFT for current data from 1.0 to 1.5 seconds: 70.0Hz FFT for current data from 1.5 to 2.0 seconds: 59.9Hz FFT for current data from 2.0 to 2.5 seconds: 56.5Hz FFT for current data from 2.5 to 3.0 seconds: 60.0Hz FFT for current data from 3.0 to 3.5 seconds: 60.2Hz FFT for current data from 3.5 to 4.0 seconds: 59.5Hz ------------------------------------------------------------------

[0024] Then, the amount of change Δfd (e.g., half-width) of the drive frequency fd from the reference drive frequency fr is calculated, and if Δfd≧Δfth, it is determined that the rotation speed of the motor 1 has changed, while if Δfd<Δfth, it is determined that the rotation speed of the motor 1 has not changed, and an abnormality diagnosis process using the following deterioration degree is executed.

[0025] (Abnormality diagnosis processing) FIG. 5A is a graph showing an example of an FFT spectrum of a drive current in a motor experiencing a load imbalance, and FIG. 5B is a graph showing an example of an FFT spectrum after removing DC components and harmonics from the FFT spectrum of FIG. 5A.

[0026] In this embodiment, DC components and harmonics that have fluctuations not caused by an abnormality in the motor 2 are removed from the FFT value of the drive current of the motor 2, and a preset number of data are added together. This eliminates the need for settings required to identify sideband waves, and makes it possible to identify deterioration and failure of the motor 2.

[0027] For example, in the FFT waveform of the drive current of motor 2 with a load imbalance as shown in Fig. 5A, the DC component and harmonics fluctuate greatly, so when the amplitude of the current data is added, the summed value fluctuates due to factors other than an abnormality in motor 2. Therefore, in the past, a process was required to identify the frequency obtained by adding the rotation frequency to the power supply frequency, and the frequency obtained by subtracting the rotation frequency from the power supply frequency.

[0028] However, in this embodiment, as shown in FIG. 5B, by removing the DC components and harmonics, the change in the frequency components (= power supply frequency ± rotation frequency) caused by an abnormality in the motor 2 becomes significant, and the abnormality in the motor 2 can be quantified simply by adding up the amplitudes.

[0029] 3A or 3B, the deterioration degree calculation unit 13a of the abnormality detection unit 13 sums up a preset number of intensity values ​​from the top in the FFT value of the current data to calculate the deterioration degree. Specifically, the deterioration degree calculation unit 13a sums up, for example, the top 10 noise intensity values, divides the summed intensity value by all the signal values, and multiplies the sum by a coefficient for adjusting the sensitivity to calculate the deterioration degree using the following equation.

[0030]

number

[0031] In the above formula, N indicates the number of data to be added together, and A indicates a coefficient for adjusting the sensitivity. After that, the abnormality detection unit 13 judges the deterioration degree of the motor 2 by comparing the calculated deterioration degree with a predetermined deterioration degree threshold value (S12 in FIG. 3A or FIG. 13B).

[0032] Next, a process for calculating the deterioration degree in this embodiment will be described. In the following example, the motor 2 has four poles and is driven by direct input with a power supply frequency of 60 Hz.

[0033] The abnormality of the motor 2 appears differently depending on the failure mode. Therefore, in this embodiment, three methods for calculating the deterioration degree are provided in accordance with the failure modes.

[0034] The first failure mode is a failure mode caused by imbalance, misalignment, or rotor bar breakage. The second failure mode is a failure mode caused by cavitation. The third failure mode is a failure mode caused by bearing deterioration. The calculation process for the deterioration degree in each failure mode will be explained below.

[0035] (First failure mode) As an example, a deterioration degree calculation process in a failure mode due to unbalance will be described. Fig. 6 is a diagram showing the load current when the motor 2 is normal and when an abnormality due to unbalance occurs. In this example, the deterioration degree calculation unit 13a performs FFT with a resolution of 0.25 Hz using the frequency analysis unit 12, sums up the top 10 intensity values ​​in the frequency range of 0 Hz to the second harmonic (0 Hz to 120 Hz), and calculates the deterioration degree using the following formula.

[0036]

number

[0037] However, the top 10 is just an example, and it is sufficient to add up 6 to 20 intensity values.

[0038] As can be seen from Figure 6, in this case, the intensity values ​​of the fundamental frequency ± rotation frequency rise, and under the conditions of this example, in the case of an abnormality, the intensity values ​​of 30 Hz and 90 Hz rise. Also, in this case, the deterioration level in the normal case was 13, and the deterioration level in the abnormal case was 22. Therefore, by setting the deterioration level threshold to 20, it is possible to determine whether the motor 2 is abnormal.

[0039] (Second failure mode) As an example, the calculation process of the deterioration degree in the case of a failure mode due to cavitation will be described. Fig. 7 is a graph showing the FFT spectrum of the load current when the motor 2 is normal and when an abnormality due to cavitation occurs. In the example of Fig. 7, the frequency analysis unit 12 performs FFT with a resolution of 0.25 Hz, and the top 60 intensity values ​​in the frequency range of the fundamental frequency ±15 Hz are added together to calculate the deterioration degree by the following formula.

[0040]

number

[0041] However, the top 60 is just an example, and the number can be changed as appropriate and the intensity values ​​can be added up.

[0042] As can be seen from Fig. 7, in this case, the intensity value within ±15 Hz of the fundamental frequency increases, and in the case of an abnormality, the intensity value between 45 Hz and 75 Hz increases. Also, in this case, the deterioration level in the normal case was 13, and the deterioration level in the abnormal case was 30. Therefore, by setting the deterioration level threshold value to 20, it is possible to determine whether the motor 2 is abnormal.

[0043] (Third failure mode) As an example, a calculation process for the deterioration degree in a failure mode due to bearing deterioration will be described below. Fig. 8 is a graph showing the FFT spectrum of the load current when the motor 2 is normal and when an abnormality occurs due to bearing deterioration.

[0044] In the example of FIG. 8, the frequency analysis unit 12 performs FFT with a resolution of 0.25 Hz, sums up the top 4000 intensity values ​​in the frequency range from the second harmonic to the twentieth harmonic (120 Hz to 1200 Hz), and calculates the degree of deterioration using the following formula.

[0045]

number

[0046] However, the top 4000 are just an example, and the number may be changed as appropriate to add up the intensity values.

[0047] As can be seen from Fig. 8, in this case, the intensity values ​​of the second harmonic to the twentieth harmonic increase, and under the conditions of this example, in the case of an abnormality, the intensity value of 120 Hz to 1200 Hz increases. Also, in this case, the deterioration level in the normal case was 20, and the deterioration level in the abnormal case was 30. Therefore, by setting the deterioration level threshold to 25, it is possible to determine whether the motor 2 is abnormal.

[0048] (Effects of the embodiment) As described above, according to this embodiment, the change amount Δfd (frequency width) of the drive frequency fd from the reference drive frequency fr is calculated and compared with a predetermined threshold value Δfth to determine whether the motor is in an abnormal state. This makes it possible to detect an abnormal state of the motor with higher accuracy than the conventional technology.

[0049] Furthermore, according to this embodiment, the deterioration level is calculated by adding up a predetermined number of intensity values ​​from the top within a predetermined frequency range, so that it is possible to diagnose motor abnormalities without setting many parameters such as the motor drive frequency, the number of motor poles, and slip.

[0050] (Modification) Next, a modified example of the present invention will be described in detail with reference to the drawings. Fig. 9 is an FFT spectrum diagram showing a state in which noise caused by inverter drive and minute noise caused by other factors are generated in a signal to be detected in the modified example. Fig. 10 is a graph showing the FFT spectrum of the load current when the motor is normal and when an abnormality occurs due to imbalance in the embodiment.

[0051] In the above embodiment, a configuration has been described in which a degradation level is calculated by adding up a predetermined number of intensity values ​​from the top. Meanwhile, in the modified example, a configuration will be described in which a degradation level is calculated by adding up intensity values ​​above a certain level.

[0052] As shown in Fig. 9, the influence of inverter driving may cause noise larger than the signal to be detected. In Fig. 9, arrow A indicates noise caused by the influence of inverter driving. In this case, if a small number of intensity values, such as the top 10, are added together, the top 10 may contain a lot of noise caused by the influence of the inverter, and the sensitivity of detecting signals caused by abnormalities may decrease. The intensity value of noise caused by the influence of inverter driving differs depending on the control method or manufacturer of the inverter used, so it cannot be removed uniformly. Therefore, it is possible to add up a larger number of intensity values ​​rather than adding up a fixed number of intensity values ​​from the top.

[0053] However, as shown in Fig. 9, in addition to the noise caused by the inverter drive, there is also minute noise caused by other factors. Fig. 9 is a diagram showing a state in which noise caused by the inverter drive and minute noise caused by other factors are present in the signal to be detected. In Fig. 9, arrow C indicates minute noise caused by other factors. The intensity value of this minute noise is highly random, and adding up many intensity values ​​will result in a large amount of minute noise being included, which may reduce the sensitivity of detecting signals due to abnormalities.

[0054] Therefore, in the modified example, signals below a certain level are removed, and intensity values ​​above a certain level are added together to calculate the degree of degradation. However, experimental results have shown that noise due to anomalies has an intensity value of -50 dB or more. Therefore, in the modified example, for example, a margin of -10 dB is taken and intensity values ​​above -60 dB are added together to remove minute noise, and then all remaining signals are added together. As a result, it was confirmed that signals to be detected can be reliably added together after removing minute noise, and a deterioration tendency can be detected.

[0055] The noise generated by the effect of inverter driving is constant regardless of the abnormality, but the signal to be detected changes depending on the abnormality. However, according to the modified example, by adding up the intensity values ​​above a certain level, a sufficient number of intensity values ​​of the signal to be detected can be obtained, and a decrease in sensitivity to detect the signal due to an abnormality can be prevented.

[0056] Next, a deterioration degree calculation process in the modified example will be described. In the following example, the motor 2 has four poles and is driven by direct input with a power supply frequency of 60 Hz.

[0057] In the modified example, a deterioration degree calculation process in a failure mode due to unbalance will be described as an example. Fig. 10 is a graph showing the FFT spectrum of the load current when the motor 2 is normal and when an abnormality due to unbalance occurs. In the example of Fig. 10, the frequency analysis unit 12 performs FFT with a resolution of 0.25 Hz, and adds up intensity values ​​of -60 dB or more in the frequency range of 0 Hz to the second harmonic (0 Hz to 120 Hz) to calculate the deterioration degree by the following formula.

[0058]

number

[0059] As can be seen from Figure 10, in this case, the intensity values ​​of the fundamental frequency ± rotation frequency rise, and under the conditions of this example, in the case of an abnormality, the intensity values ​​of 30 Hz and 90 Hz rise. Also, in this case, the deterioration level in the normal case was 24, and the deterioration level in the abnormal case was 33. Therefore, by setting the deterioration level threshold to 30, it is possible to determine whether the motor 2 is abnormal.

[0060] The embodiment and the modified example may be combined. In the case of combining the embodiment and the modified example, the abnormality detection device 4, particularly the deterioration degree calculation unit 13a, may be provided with a function of calculating the deterioration degree by adding up a preset number of intensity values ​​from the top for intensity values ​​that are equal to or higher than a certain level within a preset frequency range.

[0061] (Another variation) In the above embodiment, the frequency corresponding to the average value of multiple maximum peak values ​​corresponding to multiple frame periods is calculated as the reference drive frequency fr (this corresponds to not applying the removal condition in step S4 of FIG. 2). However, the present invention is not limited to this, and a predetermined drive frequency that is a predetermined specification value of the motor may be used as the reference drive frequency fr (this corresponds to the case where a predetermined reference drive frequency fr is used).

[0062] Alternatively, the reference driving frequency fr may be the average of the frequencies corresponding to the maximum peak values ​​excluding the maximum and minimum values ​​among the multiple maximum peak values ​​corresponding to multiple frame periods (this corresponds to applying the first elimination condition in step S4 of FIG. 2). In the case of the above embodiment, the average value 59.95 Hz excluding 70.0 Hz and 56.5 Hz becomes the reference driving frequency fr.

[0063] Alternatively, the reference driving frequency fr may be the average of the frequencies corresponding to the maximum peak values ​​excluding the most distant value among the multiple maximum peak values ​​corresponding to multiple frame periods (this corresponds to applying the second elimination condition in step S4 of FIG. 2). In the above embodiment, the average value excluding 70.0 Hz, 59.46 Hz, becomes the reference driving frequency fr.

[0064] Alternatively, the reference driving frequency fr may be the average value of the frequencies corresponding to the maximum peak values ​​excluding the maximum half-width among the multiple maximum peak values ​​corresponding to multiple frame periods (this corresponds to applying the third elimination condition in step S4 of FIG. 2). In the case of the above embodiment, if the waveform is 70.0 Hz and the half-width drop is wide, the average value excluding 70.0 Hz, 59.46 Hz, becomes the reference driving frequency fr.

[0065] In the modified example of the above embodiment, the abnormal state of the motor is determined by performing frequency analysis on current data of the current supplied from the AC power supply to the motor and determining whether the rotation speed of the motor has changed, but the present invention is not limited to this, and the abnormal state of the motor may also be determined by performing frequency analysis on voltage data of the voltage supplied from the AC power supply to the motor and similarly determining whether the rotation speed of the motor has changed. [Industrial Applicability]

[0066] As described above in detail, according to the present invention, the change amount Δfd (frequency width) of the drive frequency fd from the reference drive frequency fr is calculated and compared with a predetermined threshold value Δfth to determine whether the rotation speed of the motor has changed. This makes it possible to detect an abnormal state of the motor with higher accuracy than the prior art. [Explanation of symbols]

[0067] 1 AC power supply 2 Motors 3 Current Sensor 4. Anomaly detection device 10 Processor (signal processing section) 11 AD converter 12 Frequency Analysis Section 12M memory 13 Anomaly detection section 13a Deterioration degree calculation section 13M memory 14 Display section

Claims

1. An abnormality detection device including a signal processing unit that detects an abnormal state of a motor based on a current or voltage supplied to the motor from a power source, The signal processing unit includes: By subjecting the current or voltage data to frequency analysis, a frequency width is calculated which is a change in a drive frequency from a reference drive frequency determined based on a maximum peak value of the frequency analysis result, and which is a difference between a pair of frequencies among the drive frequencies; An abnormality detection device that detects an abnormal state of the motor by determining that the rotational speed of the motor has changed when the calculated frequency width is equal to or greater than a predetermined threshold value, and determining that the rotational speed of the motor has not changed when the calculated frequency width is less than a predetermined threshold value.

2. The frequency width is the difference between a pair of frequencies corresponding to a predetermined pair of values ​​from the maximum peak value. The abnormality detection device according to claim 1 .

3. The frequency width is a half-width which is a difference between a pair of frequencies corresponding to a pair of values ​​lowered by 1 / 2 of the maximum peak value. The abnormality detection device according to claim 2 .

4. The reference driving frequency is a frequency corresponding to an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods. The abnormality detection device according to any one of claims 1 to 3.

5. The reference drive frequency is calculating an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding a maximum value and a minimum value of the plurality of maximum peak values, and determining a frequency corresponding to the calculated average value of the maximum peak values; The abnormality detection device according to any one of claims 1 to 3.

6. The reference drive frequency is calculating an average of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding the most distant value of the plurality of maximum peak values, and determining a frequency corresponding to the calculated average of the maximum peak values; The abnormality detection device according to any one of claims 1 to 3.

7. The reference drive frequency is calculating an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding a value corresponding to a maximum frequency width among the plurality of maximum peak values, and determining a frequency corresponding to the calculated average value of the maximum peak values; The abnormality detection device according to any one of claims 1 to 3.

8. The signal processing unit, when it is determined that the rotation speed of the motor has not changed, controls a deterioration degree calculation unit that calculates a deterioration degree by adding up a predetermined number of intensity values ​​from a higher level in a predetermined frequency range, The abnormality detection device according to any one of claims 1 to 7.

9. the degradation degree calculation unit calculates a degradation degree by summing up a preset number of intensities from the top for intensity values ​​equal to or higher than a certain level; The abnormality detection device according to claim 8.

10. The deterioration degree calculation unit excludes intensity values ​​at a fundamental frequency and harmonics from the calculation of the deterioration degree. The abnormality detection device according to claim 8 or 9.

11. the degradation degree calculation unit calculates the degradation degree by summing up intensity values ​​equal to or higher than a certain level within a preset frequency range; The abnormality detection device according to claim 8.

12. The signal processing unit determines whether or not the motor has deteriorated by comparing the calculated deterioration degree with a predetermined deterioration degree threshold value. The abnormality detection device according to any one of claims 8 to 11.

13. 1. An abnormality detection method for an abnormality detection device including a signal processing unit that detects an abnormal state of a motor based on a current or voltage supplied to the motor from a power supply, comprising: A step in which the signal processing unit performs frequency analysis on the current or voltage data to calculate a frequency width that is a change in drive frequency from a reference drive frequency determined based on a maximum peak value of the frequency analysis result, the frequency width being a difference between a pair of frequencies among the drive frequencies; detecting an abnormal state of the motor by determining that a rotation speed of the motor has changed when the calculated frequency width is equal to or greater than a predetermined threshold value, and determining that the rotation speed of the motor has not changed when the calculated frequency width is less than the predetermined threshold value; The anomaly detection method includes:

14. The frequency width is a difference between a pair of frequencies corresponding to a pair of values ​​that are lowered by a predetermined value from the maximum peak value. The anomaly detection method according to claim 13.

15. The frequency width is a half-width which is a difference between a pair of frequencies corresponding to a pair of values ​​which are half the value lower than the maximum peak value. The anomaly detection method according to claim 14.

16. The reference driving frequency is a frequency corresponding to an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods. The abnormality detection method according to any one of claims 13 to 15.

17. The reference drive frequency is calculating an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding a maximum value and a minimum value of the plurality of maximum peak values, and determining a frequency corresponding to the calculated average value of the maximum peak values; The abnormality detection method according to any one of claims 13 to 15.

18. The reference drive frequency is calculating an average of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding the most distant value of the plurality of maximum peak values, and determining a frequency corresponding to the calculated average of the maximum peak values; The abnormality detection method according to any one of claims 13 to 15.

19. The reference drive frequency is calculating an average value of a plurality of maximum peak values ​​corresponding to a plurality of frame periods excluding a value corresponding to a maximum frequency width among the plurality of maximum peak values, and determining a frequency corresponding to the calculated average value of the maximum peak values; The abnormality detection method according to any one of claims 13 to 15.

20. When the signal processing unit determines that the rotation speed of the motor has not changed, a step of calculating a degradation degree by adding up a predetermined number of intensity values ​​from the top in a predetermined frequency range by a degradation degree calculation unit; The abnormality detection method according to any one of claims 13 to 19, further comprising:

21. the step of calculating the deterioration level calculates the deterioration level by adding up a preset number of intensities from the top for intensity values ​​equal to or higher than a certain level; The anomaly detection method of claim 20.

22. The step of calculating the deterioration degree includes excluding intensity values ​​at the fundamental frequency and harmonics from the calculation of the deterioration degree.

22. The method for detecting an anomaly according to claim 20 or 21.

23. the step of calculating the deterioration degree calculates the deterioration degree by adding up intensity values ​​having a certain level or more in a preset frequency range; 23. The method of claim 22.

24. a step of the signal processing unit comparing the calculated deterioration degree with a predetermined deterioration degree threshold value to determine whether or not the motor has deteriorated; The abnormality detection method according to any one of claims 20 to 23, further comprising:

Images