WO2022190795A1 - 異常検出装置及び方法 - Google Patents
異常検出装置及び方法 Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 78
- 230000005856 abnormality Effects 0.000 title claims abstract description 71
- 238000000034 method Methods 0.000 title description 30
- 238000001228 spectrum Methods 0.000 claims abstract description 122
- 230000002159 abnormal effect Effects 0.000 claims abstract description 27
- 238000012545 processing Methods 0.000 claims abstract description 22
- 238000001914 filtration Methods 0.000 claims description 3
- 230000015654 memory Effects 0.000 description 9
- 238000007781 pre-processing Methods 0.000 description 6
- 238000006243 chemical reaction Methods 0.000 description 5
- 230000035945 sensitivity Effects 0.000 description 4
- 230000000737 periodic effect Effects 0.000 description 3
- 230000005236 sound signal Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
- H02P29/02—Providing protection against overload without automatic interruption of supply
- H02P29/024—Detecting a fault condition, e.g. short circuit, locked rotor, open circuit or loss of load
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Definitions
- the present invention relates to an abnormality detection device and method for detecting an abnormal state of a rotating machine such as a motor.
- Patent Literature 1 discloses an anomaly detection device that can identify the frequency corresponding to the cycle of the sound emitted by rotation based on an audio signal even when periodic noise is present.
- This anomaly detection device detects an envelope curve of an audio signal representing a periodic sound emitted from a rotating body having a predetermined number of wings and a periodic sound emitted from another object.
- the frequency spectrum of the audio signal is calculated, and frequency candidates corresponding to the period of the sound emitted from the rotating body in that frame are detected for each frame.
- the duration time for which the fluctuation of the power of the frequency spectrum component in the candidate detected for that frame is less than a certain value is obtained, and the candidate with the longest duration time is the sound emitted from the rotating body. It is specified as the frequency corresponding to the period.
- the user In order to obtain correct diagnostic results, the user must keep the rotation speed of the motor constant at least while performing diagnostic measurements. Further, when the degree of abnormality in the diagnostic result increases, the user must determine whether the abnormality is actually caused by an abnormality in the motor or by a change in the rotation speed of the motor.
- An object of the present invention is to solve the above problems and to provide an abnormality detection device and method capable of detecting an abnormal state of a motor with higher accuracy than conventional techniques.
- An abnormality detection device includes: An abnormality detection device that detects an abnormal state of the motor based on the current or voltage supplied to the motor from a power supply, By frequency-analyzing the current or voltage data in different first and second time periods, the first driving frequency corresponding to the maximum peak value of the spectrum in the first time period and the second time period retrieving a second drive frequency corresponding to the maximum peak value of the spectrum for the period; After correcting the frequency axis of one spectrum so as to match the maximum peak values of the respective spectra based on the first spectrum having the first driving frequency and the spectrum having the second driving frequency , calculating an averaged spectrum for the corrected spectrum and the uncorrected spectrum, Based on the averaged spectrum, an abnormal peak value equal to or greater than a predetermined threshold is searched for at a frequency different from the driving frequency having the maximum peak value, and the abnormal state of the motor is determined according to the presence or absence of the abnormal peak value.
- a signal processing unit that determines Prepare.
- the abnormal state of the motor can be detected with higher accuracy than the conventional technology.
- FIG. 1 is a block diagram showing a configuration example of a motor abnormality detection device 4 according to Embodiment 1.
- FIG. FIG. 2 is a flow chart showing anomaly detection processing executed by the processor 10 of FIG. 1;
- FIG. 3 is a graph showing an example of a plurality of motor current spectra calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 2;
- 10 is a flow chart showing preprocessing for abnormality detection executed by the processor 10 of the motor abnormality detection device 4 according to the second embodiment.
- 9 is a flowchart showing abnormality detection processing executed by the processor 10 of the motor abnormality detection device 4 according to the second embodiment.
- FIG. 5 is a graph showing a first example of a motor current spectrum calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 4B;
- FIG. FIG. 5 is a graph showing a second example of a motor current spectrum calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 4B;
- FIG. 1 is a block diagram showing a configuration example of a motor abnormality detection device 4 according to the first embodiment.
- FIG. 1 shows an 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.
- the abnormality detection device 4 includes an AD converter 11, a processor 10 having a frequency analysis section 12 and an abnormality detection section 13 and constituting a "signal processing section", and a display section 14. be.
- the frequency analysis unit 12 and the abnormality detection unit 13 have memories 12m and 13m, respectively.
- the current sensor 3 detects the current value of the 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 a current value, and then outputs the current data to the frequency analysis unit 12 .
- the frequency analysis unit 12 performs fast Fourier transform (FFT) processing on the input current data over a predetermined time period T1 and two divided time periods T2 and T3.
- FFT fast Fourier transform
- the abnormality detection unit 13 determines the abnormal state of the motor based on the FFT-processed spectrum, and displays the determination result on the display unit 14 .
- the processor 10 consisting of the frequency analysis unit 12 and the abnormality detection unit 13 executes the abnormality detection process shown in FIG. do.
- FIG. 2 is a flow chart showing anomaly detection processing executed by the processor 10 of FIG. 3 is a graph showing an example of a plurality of spectra of motor current calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 2.
- FIG. 2 is a flow chart showing anomaly detection processing executed by the processor 10 of FIG. 3 is a graph showing an example of a plurality of spectra of motor current calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 2.
- FIG. 1 The abnormality detection process according to the first embodiment will be described below with reference to FIGS. 2 and 3.
- FIG. 2 is a diagrammatic representation of the abnormality detection process according to the first embodiment.
- step S1 of FIG. 2 the current data of the AD conversion value of the predetermined time period T1 detected by the current sensor 3 is input, and in step S2A, the frequency analysis unit 12 detects the AD conversion value of the current value of the predetermined time period T1.
- FFT processing is performed using the memory 12m to calculate the reference spectrum 100 (FIG. 3A) of the FFT value (power), and in step S2B, the AD conversion value of the current value for the predetermined period T1 is
- the memory 12m is used to perform FFT processing for each of the two predetermined time periods T2 and T3, and the FFT value (power) spectra 101 and 102 (Fig. 3(b) ) and FIG. 3(c)).
- step S3 the abnormality detection unit 13 searches for the drive frequency corresponding to the maximum peak value of each time period T2, T3 based on the spectrum of each time period T2, T3.
- steps S3 to S9 is executed by the abnormality detection unit 13.
- the corrected high A spectrum 102A FIG.
- step S5 the spectrum 102A having the corrected high driving frequency f high and the spectrum 101 having the low driving frequency f low after correction are added together with the maximum peak values and divided by 2 to obtain these 2
- An averaged spectrum 103 (FIG. 3(e)) of the spectra is calculated and stored in the memory 13m.
- step S6 the abnormality detection unit 13 searches for a peak value of the abnormal peak 104 equal to or greater than a predetermined threshold value at a frequency different from the drive frequency having the maximum peak value, based on the averaged spectrum 103 ( FIG. 3(e)).
- the abnormal peak 104 has a peak value having, for example, the second or third strongest power, for example, next to the maximum peak.
- step S7 the abnormality detection unit 13 determines whether or not there is an abnormal peak. If YES, the process proceeds to step S8, and if NO, the process proceeds to step S9.
- step S8 it is determined that the motor is in an abnormal state, the result of the determination is displayed on the display unit 14, and the abnormality detection process ends.
- step S9 it is determined that the motor is not in an abnormal state, the determination result is displayed on the display unit 14, and the abnormality detection process is terminated.
- the time period T1 is divided into two time periods T2 and T3 for the current data of the motor drive current acquired in the predetermined time period T1, and frequency analysis is performed.
- a drive frequency that has the maximum peak value for each of T2 and T3 is searched.
- the frequency axis of the spectrum of the frequency analysis result with the high drive frequency is multiplied by the division value of the low drive frequency divided by the high drive frequency of the two searched drive frequencies, and the spectrum of the frequency analysis result with the low drive frequency is multiplied. Add and average.
- the driving frequency fluctuates in the data for 4 seconds, and the peak of the abnormality feature is dulled
- the FFT between 0 to 2 seconds and the frequency analysis for 2 to 4 seconds
- the frequencies of the maximum peaks are 51 Hz and 52 Hz, respectively
- the spectrum obtained by multiplying the frequency axis of 52 Hz by 51/52 and the spectrum of the frequency analysis result of 51 Hz are added and averaged.
- the features of the abnormality appear sharply, and the features of the abnormality can be captured more accurately and with higher sensitivity than the envelope FFT.
- the peak 104 (FIG. 3(e)) that could not be seen with the conventional technology can be captured.
- the drive frequency fluctuates, it becomes easy to separate the noise component from the peak that always appears, and the abnormality detection process can be performed with higher accuracy than in the conventional technology.
- FIG. 4A is a flow chart showing preprocessing for abnormality detection executed by the processor 10 of the motor abnormality detection device 4 according to the second embodiment.
- FIG. 4B is a flowchart showing an abnormality detection process executed by the processor 10 of the motor abnormality detection device 4 according to the second embodiment.
- the abnormality detection process according to the second embodiment differs from the abnormality detection process according to the first embodiment in the following points.
- the abnormality detection device 4 is configured in the same manner as in FIG. (1)
- the anomaly detection pre-processing in FIG. 4A is executed before the anomaly detection process in FIG. 4B.
- a peak width (frequency width) is calculated, and based on the calculated peak width, a lower cutoff frequency fc is determined and stored in the memory 13m.
- the abnormality detection process of FIG. 4B is characterized in that the process of step S2C is inserted between steps S2A and S2B. Differences will be described below.
- step S11 of the abnormality detection preprocessing in FIG. 4A the AD conversion value of the current value detected by the current sensor 3 during the predetermined time period T1 is input.
- step S12 the frequency analysis unit 12 performs FFT processing on the AD conversion value of the current value of the predetermined period T1 using the memory 12m to calculate the spectrum of the FFT value (power).
- step SS13 the frequency width of the peak having the maximum peak value is calculated for FFT values equal to or greater than a predetermined threshold, and in step S14, based on the calculated frequency width of the peak, the lower cutoff frequency fc is set, and the anomaly detection preprocessing is terminated.
- the low cutoff frequency fc is set, for example, so that the lower cutoff frequency fc increases as the calculated peak width (frequency width) increases. , to increase the detection sensitivity.
- step S2C of the abnormality detection process in FIG. 4B the reference spectrum 100 is filtered with a high-pass filter having the low cutoff frequency fc determined in the abnormality detection preprocessing, and the filtered spectrum is used as a reference.
- the processes of steps S2B to S9 are executed in the same manner as in FIG.
- FIG. 5A is a graph showing a first example of a motor current spectrum calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 4B.
- FIG. 5B is a graph showing a second example of the motor current spectrum calculated when the processor 10 of FIG. 1 executes the abnormality detection process of FIG. 4B.
- the band width for cutting the low-frequency component is adjusted according to the degree of fluctuation of the drive frequency to increase the detection sensitivity. For example, as shown in the spectrum and 201 of FIG. 5A, when the fluctuation amount of the drive frequency is within ⁇ 0.5 Hz, the low cutoff frequency fc is set to 1 Hz. Moreover, as shown in the spectrum 202 of FIG. 5B, when the fluctuation amount of the driving frequency is within ⁇ 1.0 Hz, it is preferable to set the low cutoff frequency fc to 2 Hz.
- the calculated spectrum is high-pass filtered using a predetermined low cutoff frequency fc. Cut frequency components.
- the lower cutoff frequency fc is set so that the lower cutoff frequency fc increases as the calculated frequency width of the peak increases, thereby increasing the detection sensitivity. can be done.
- the drive frequency fluctuates, it becomes easy to separate the noise component from the peak that always appears, and the abnormality detection process can be performed with higher accuracy than in the conventional technology.
- the current data of the current supplied from the AC power supply to the motor is frequency-analyzed to determine the abnormal state of the motor, but the present invention is not limited to this.
- the voltage data of the voltage may be frequency-analyzed to similarly determine the abnormal condition of the motor.
- the frequency axis of the spectrum having the higher drive frequency is multiplied by a division value obtained by dividing the lower drive frequency by the higher drive frequency of the two drive frequencies including the first and second drive frequencies.
- the spectrum having the corrected high driving frequency is calculated, and the spectrum having the corrected high driving frequency and the spectrum having the low driving frequency are calculated.
- the averaged spectrum is calculated by combining the maximum peak values of the two spectra.
- the present invention is not limited to this, and the frequency axis of the spectrum having the low drive frequency is multiplied by a division value obtained by dividing the high drive frequency by the low drive frequency of the first and second drive frequencies.
- a spectrum having the corrected low driving frequency is calculated, and the corrected spectrum having the low driving frequency and the spectrum having the high driving frequency are calculated. and the averaged spectrum may be calculated by combining the maximum peak values of the two spectra.
- one spectrum is adjusted so as to match the maximum peak values of each spectrum. After correcting the frequency axis, an averaged spectrum of the corrected spectrum and the uncorrected spectrum may be calculated.
- the abnormality detection process when the drive frequency fluctuates, it becomes easy to separate the noise component from the peak that always appears, and the abnormality detection process can be performed with higher precision than in the prior art. It can be carried out.
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Abstract
Description
電源からモータに供給される電流又は電圧に基づいて、前記モータの異常状態を検出する異常検出装置であって、
前記電流又は電圧のデータを異なる第1及び第2の時間期間で周波数分析することで、前記第1の時間期間のスペクトルの最大ピーク値に対応する第1の駆動周波数と、前記第2の時間期間のスペクトルの最大ピーク値に対応する第2の駆動周波数とを検索し、
前記第1の駆動周波数を有する第1のスペクトルと、前記第2の駆動周波数を有するスペクトルとに基づいて、前記各スペクトルの各最大ピーク値を合わせるように一方のスペクトルの周波数軸を補正した後、補正後のスペクトルと未補正のスペクトルとに係る平均化スペクトルを計算し、
前記平均化スペクトルに基づいて、最大ピーク値を有する駆動周波数とは異なる周波数において、所定のしきい値以上の異常ピーク値を検索し、前記異常ピーク値の有無に応じて、前記モータの異常状態を判断する信号処理部を、
備える。
図1は実施形態1に係るモータの異常検出装置4の構成例を示すブロック図である。図1は異常検出装置4及びその周辺回路を示しており、交流電源1からの交流電力は電流センサ3を介してモータ2に供給される。図1において、異常検出装置4は、AD変換器11と、周波数分析部12及び異常検出部13を有して「信号処理部」を構成するプロセッサ10と、表示部14とを備えて構成される。ここで、周波数分析部12及び異常検出部13はそれぞれメモリ12m,13mを有する。
図4Aは実施形態2に係るモータの異常検出装置4のプロセッサ10により実行される異常検出前置処理を示すフローチャートである。また、図4Bは実施形態2に係るモータの異常検出装置4のプロセッサ10により実行される異常検出処理を示すフローチャートである。実施形態2に係る異常検出処理が、実施形態1に係る異常検出処理と比較して以下のことが異なる。なお、異常検出装置4は、図1と同様に構成される。
(1)図4Aの異常検出前置処理は、図4Bの異常検出処理よりも前段で実行され、これにより、異常検出部13は、所定のしきい値以上のFFT値において、最大ピーク値を有するピーク幅(周波数幅)を計算し、計算されたピーク幅に基づいて、低域遮断周波数fcを決定してメモリ13mに格納する。
(2)図4Bの異常検出処理は、図2の異常検出処理と比較して、ステップS2Cの処理を、ステップS2AとステップS2Bとの間に挿入したことを特徴とする。
以下、相違点について説明する。
以上の実施形態では、交流電源からモータに供給される電流の電流データを周波数分析してモータの異常状態について判断しているが、本発明はこれに限らず、交流電源からモータに供給される電圧の電圧データを周波数分析して、同様にモータの異常状態について判断してもよい。
2 モータ
3 電流センサ
4 異常検出装置
10 プロセッサ(信号処理部)
11 AD変換器
12 周波数分析部
12m メモリ
13 異常検出部
13m メモリ
14 表示部
Claims (10)
- 電源からモータに供給される電流又は電圧に基づいて、前記モータの異常状態を検出する異常検出装置であって、
前記電流又は電圧のデータを異なる第1及び第2の時間期間で周波数分析することで、前記第1の時間期間のスペクトルの最大ピーク値に対応する第1の駆動周波数と、前記第2の時間期間のスペクトルの最大ピーク値に対応する第2の駆動周波数とを検索し、
前記第1の駆動周波数を有する第1のスペクトルと、前記第2の駆動周波数を有するスペクトルとに基づいて、前記各スペクトルの各最大ピーク値を合わせるように一方のスペクトルの周波数軸を補正した後、補正後のスペクトルと未補正のスペクトルとに係る平均化スペクトルを計算し、
前記平均化スペクトルに基づいて、最大ピーク値を有する駆動周波数とは異なる周波数において、所定のしきい値以上の異常ピーク値を検索し、前記異常ピーク値の有無に応じて、前記モータの異常状態を判断する信号処理部を、
備える異常検出装置。 - 前記平均化スペクトルを計算するときに、前記信号処理部は、
(1)前記第1及び第2の駆動周波数のうち、低い駆動周波数を高い駆動周波数で除算した除算値を、前記高い駆動周波数を有するスペクトルの周波数軸に乗算するように、前記高い駆動周波数を有するスペクトルの周波数軸を補正することで、補正後の高い駆動周波数を有するスペクトルを計算し、前記補正後の高い駆動周波数を有するスペクトルと、前記低い駆動周波数を有するスペクトルとに基づいて、当該2つのスペクトルの各最大ピーク値を合わせて前記平均化スペクトルを計算し、もしくは
(2)前記第1及び第2の駆動周波数のうち、高い駆動周波数を低い駆動周波数で除算した除算値を、前記低い駆動周波数を有するスペクトルの周波数軸に乗算するように、前記低い駆動周波数を有するスペクトルの周波数軸を補正することで、補正後の低い駆動周波数を有するスペクトルを計算し、前記補正後の低い駆動周波数を有するスペクトルと、前記高い駆動周波数を有するスペクトルとに基づいて、当該2つのスペクトルの各最大ピーク値を合わせて前記平均化スペクトルを計算する、
請求項1に記載の異常検出装置。 - 前記信号処理部は、第1及び第2の時間期間で周波数分析する前に、前記周波数分析する前記電流又は電圧のデータに対して、所定の低域遮断周波数でハイパスフィルタリングする、
請求項1又は2に記載の異常検出装置。 - 前記信号処理部は、
前記電流又は電圧のデータを、前記第1及び第2の時間期間を含む時間期間で周波数分析し、前記周波数分析結果のスペクトルに基づいて、最大ピーク値を有するピークの周波数幅を計算し、前記計算されたピークの周波数幅に基づいて、前記低域遮断周波数を設定する、
請求項3に記載の異常検出装置。 - 前記信号処理部は、前記計算されたピークの周波数幅が広くなるにつれて、前記低域遮断周波数を高くなるように設定する、
請求項4に記載の異常検出装置。 - 電源からモータに供給される電流又は電圧に基づいて、前記モータの異常状態を検出する異常検出方法であって、
信号処理部が、前記電流又は電圧のデータを異なる第1及び第2の時間期間で周波数分析することで、前記第1の時間期間のスペクトルの最大ピーク値に対応する第1の駆動周波数と、前記第2の時間期間のスペクトルの最大ピーク値に対応する第2の駆動周波数とを検索するステップと、
前記第1の駆動周波数を有する第1のスペクトルと、前記第2の駆動周波数を有するスペクトルとに基づいて、前記各スペクトルの各最大ピーク値を合わせるように一方のスペクトルの周波数軸を補正した後、補正後のスペクトルと未補正のスペクトルとに係る平均化スペクトルを計算するステップと、
前記信号処理部が、前記平均化スペクトルに基づいて、最大ピーク値を有する駆動周波数とは異なる周波数において、所定のしきい値以上の異常ピーク値を検索し、記異常ピーク値の有無に応じて、前記モータの異常状態を判断するステップと、
含む異常検出方法。 - 前記平均化スペクトルを計算するステップは、前記信号処理部が、
(1)前記第1及び第2の駆動周波数のうち、低い駆動周波数を高い駆動周波数で除算した除算値を、前記高い駆動周波数を有するスペクトルの周波数軸に乗算するように、前記高い駆動周波数を有するスペクトルの周波数軸を補正することで、補正後の高い駆動周波数を有するスペクトルを計算し、前記補正後の高い駆動周波数を有するスペクトルと、前記低い駆動周波数を有するスペクトルとに基づいて、当該2つのスペクトルの各最大ピーク値を合わせて前記平均化スペクトルを計算し、もしくは
(2)前記第1及び第2の駆動周波数のうち、高い駆動周波数を低い駆動周波数で除算した除算値を、前記低い駆動周波数を有するスペクトルの周波数軸に乗算するように、前記低い駆動周波数を有するスペクトルの周波数軸を補正することで、補正後の低い駆動周波数を有するスペクトルを計算し、前記補正後の低い駆動周波数を有するスペクトルと、前記高い駆動周波数を有するスペクトルとに基づいて、当該2つのスペクトルの各最大ピーク値を合わせて前記平均化スペクトルを計算する、
請求項6に記載の異常検出方法。 - 前記信号処理部が、第1及び第2の時間期間で周波数分析する前に、前記周波数分析する前記電流又は電圧のデータに対して、所定の低域遮断周波数でハイパスフィルタリングするステップを、
さらに含む請求項6又は7に記載の異常検出方法。 - 前記信号処理部が、前記電流又は電圧のデータを、前記第1及び第2の時間期間を含む時間期間で周波数分析し、前記周波数分析結果のスペクトルに基づいて、最大ピーク値を有するピークの周波数幅を計算し、前記計算されたピークの周波数幅に基づいて、前記低域遮断周波数を設定するステップを、
さらに含む請求項8に記載の異常検出方法。 - 前記低域遮断周波数を設定するステップは、前記計算されたピークの周波数幅が広くなるにつれて、前記低域遮断周波数を高くなるように設定する、
請求項9に記載の異常検出方法。
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