Method for diagnosing abnormality of rolling bearing, and computer program for abnormality diagnosis

Abstract

To propose an abnormality diagnosis method which can conduct measurement contactlessly and rapidly, and which can detect abnormality of a bearing even in cases when an S / N ratio of a bearing damage signal is low and in cases when bearing damage is at an initial stage.SOLUTION: A method for diagnosing abnormality of a bearing includes: an operation sound acquisition step ST1 of acquiring an operation sound signal of a bearing; a first resonance frequency estimation step ST2 of calculating kurtoses of respective envelope spectra of operation sound signals of respective bandwidths obtained by dividing the operation sound signal by a filter bank, and estimating a resonance frequency by using a kurtosis index including a maximum kurtosis from among the calculated kurtoses; a second resonance frequency estimation step ST3 of estimating the resonance frequency by using a spectrum correlation in which the operation sound signal is represented by a contamination frequency and a vibration frequency distribution, from the operation sound signal; and an abnormality determination step ST4 of comparing the estimated resonance frequencies with each other, and determining a case considered to match each other to be an abnormal case, meanwhile, determining a case not considered to match each other to be a normal case.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to a method for diagnosing an abnormality in a rolling bearing and a computer program for diagnosing an abnormality in a rolling bearing. [Background technology]

[0002] Rolling bearings (hereafter simply referred to as "bearings") are extremely important mechanical components that are incorporated into various rotating machines, including turbines, fans, and pumps. Because these machines rotate at high speeds, failure of a bearing can lead to a major accident. For this reason, various abnormality diagnosis methods have been proposed that use temperature, wear debris, acoustic emission (AE), vibration, sound, etc.

[0003] Currently, diagnostic methods that use vibration are commonly used, but this diagnostic method requires contact with an acceleration sensor, which makes diagnosis difficult in situations where contact is difficult. In addition, it requires labor to attach and detach the acceleration sensor, which makes measurement time-consuming, among other problems.

[0004] When a bearing is damaged, the operating noise and vibration waveforms are characterized by the inclusion of periodic non-stationary waveforms that are generated when the rolling elements pass through the damaged area. These non-stationary waveforms are characterized by the fact that they contain many resonant frequency components of the machine housing in which the bearing is built.

[0005] Envelope analysis proposed in Non-Patent Document 1 is widely used as an analysis method for diagnosing bearing abnormalities by focusing on the periodicity of vibration waveforms. Since envelope analysis is a method that uses the envelope of a signal waveform, there is a problem that the periodicity cannot be detected when the non-stationary waveform is buried in broadband background noise or when the non-stationary waveform excited due to a small degree of damage is weak. In this case, it is effective to extract the non-stationary waveform by applying a bandpass filter in a frequency band including the resonant frequency, but it is difficult to determine the resonant frequency and estimate the optimal center frequency and bandwidth.

[0006] Meanwhile, methods for extracting non-stationary waveforms have also been widely studied. In Non-Patent Document 2, a spectral kurtosis based on the kurtosis index of a waveform is proposed, and its effectiveness is demonstrated.

[0007] In addition, Non-Patent Document 3 proposes a fast cartogram, which is a cartogram that generalizes the spectral kurtosis and reduces the calculation cost. The fast cartogram is an analysis method that estimates a center frequency and a frequency resolution suitable for extracting a non-stationary waveform. However, since the fast cartogram has a stronger sensitivity to a non-periodic signal than to a periodic signal, its effectiveness in extracting a periodic non-stationary waveform is limited.

[0008] In addition, Non-Patent Document 4 proposes "Protrugram" as a method for extracting periodic non-stationary waveforms, which uses the kurtosis of the envelope spectrum as an evaluation index instead of the kurtosis of the time series signal. However, since Protrugram uses an empirically selected width for the bandwidth of the bandpass filter, there is a problem in designing the bandwidth.

[0009] Furthermore, Non-Patent Document 5 proposes spectral correlation as another method focusing on periodic non-stationary waveforms. Spectral correlation can analyze the spectral frequency components contained in the frequency components of the signal envelope from the frequency expression of the autocorrelation function. Therefore, it is possible to estimate the dominant spectral frequency of a non-stationary waveform from the frequency of the envelope of the non-stationary waveform. However, spectral correlation is an estimation method with low estimation accuracy or high calculation cost, and there are problems in practical use. [Prior art documents] [Non-patent literature]

[0010] [Non-Patent Document 1] PDMcFadden, JDSmith: Vibration monitoring of rolling element bearings by the high frequency resonance technique-a review, Tribology International 17, pp.3-10, (1984). [Non-Patent Document 2] J.Antoni: The spectral kurtosis: a useful tool for characterizing non-stationary signals, Mechanical Systems and Signal Processing 20, pp.282-307, (2006) [Non-Patent Document 3] J.Antoni: Fast computation of the kurtogram for the detection of transient faults, Mechanical Systems and Signal Processing 21, pp.108-124, (2007) [Non-Patent Document 4] Tomasz Barszcz, Adam Jablonski: A nobel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram, Mechanical Systems and Signal Processing 25, pp.431-451, (2011) [Non-Patent Document 5] J.Antoni: Cyclic spectral analysis in practice, Mechanical Systems and Signal Processing 21, pp.597-630, (2007) Summary of the Invention [Problem to be solved by the invention]

[0011] As described above, the currently common diagnostic method using vibration has problems such as difficulty in diagnosis in situations where contact is difficult, and the labor required to attach and detach the acceleration sensor takes time for measurement. In addition, in bearing abnormality diagnosis, extraction of a non-stationary waveform is required when the S / N ratio of the bearing damage signal is low or when the bearing damage is in the early stages, but a definitive estimation method for extracting a non-stationary waveform has not yet been established. From the viewpoint of preventing bearing-related accidents, it is urgent to establish a frequency analysis method suitable for extracting a non-stationary waveform that is effective for bearing damage signals, and a bearing abnormality diagnosis method. In addition, in bearing abnormality diagnosis, it is more preferable if the degree of abnormality related to bearing damage can be quantitatively estimated.

[0012] In view of the above, it is an object of the present invention to provide an abnormality diagnosis method and a computer program for bearing abnormality diagnosis that can perform measurements non-contact and quickly, and that can detect bearing abnormalities even when the S / N ratio of the bearing damage signal is low or when bearing damage is in the early stages.A further object of the present invention is to provide an abnormality diagnosis method and a computer program for bearing abnormality diagnosis that can quantitatively estimate the degree of abnormality related to bearing damage. [Means for solving the problem]

[0013] In order to solve the above problems, a bearing abnormality diagnosis method according to the present invention is a rolling bearing abnormality diagnosis method for diagnosing an abnormality due to damage to a rolling bearing, comprising: an operation sound acquisition step of acquiring an operation sound signal of the rolling bearing; a first resonant frequency estimation step of calculating the kurtosis of an envelope spectrum of the operation sound signal in each band obtained by dividing the operation sound signal acquired in the operation sound acquisition step by a filter bank arranged without gaps from low to high frequencies using band-pass filters of a predetermined bandwidth, and estimating a resonant frequency using a kurtosis index having the largest kurtosis among the calculated kurtosis; a second resonant frequency estimation step of estimating a resonant frequency from the operation sound signal acquired in the operation sound acquisition step by using a spectral correlation in which the operation sound signal is expressed by a mixing frequency and a vibration frequency distribution; an abnormality determination step of comparing the respective resonant frequencies estimated in the first and second resonant frequency estimation steps, and determining that there is an abnormality when the resonant frequencies are deemed to match, and determining that there is a normality when the resonant frequencies are not deemed to match; The present invention is characterized in that it includes:

[0014] In this bearing abnormality diagnosis method, the signal used for abnormality diagnosis is the operation sound signal acquired in the operation sound acquisition step, so that non-contact and rapid measurement is possible. In addition, in the abnormality determination step, the logical product of the respective resonant frequencies estimated in the first and second resonant frequency estimation steps is taken to determine abnormality, so that the reliability of the diagnosis result is high. That is, in the first resonant frequency estimation step, the resonant frequency is estimated from the calculated value as a relative quantity using the kurtosis index, and in the second resonant frequency estimation step, the resonant frequency is estimated from the calculated value as an absolute quantity using the spectrum correlation, so that the resonant frequency is identified when similar results are obtained even with methods of completely different natures, and the identification accuracy is high. In addition, in this bearing abnormality diagnosis method, in the first resonant frequency estimation step, the kurtosis of the envelope spectrum of the operation sound signal of each band divided by a filter bank arranged without gaps from low to high frequencies by a bandpass filter of a predetermined bandwidth is calculated. The envelope of the operation sound signal preprocessed by the bandpass filter is one in which the influence of non-periodic noise components is eliminated and periodic noise components are clearly detected. As a result, according to this bearing abnormality diagnosis method, even when the S / N ratio of the bearing damage signal is low or the bearing damage is in the early stages, it is possible to extract the non-stationary waveform of bearing damage using frequency analysis techniques and detect bearing abnormalities.

[0015] In the first resonant frequency estimation step, it is preferable to estimate the resonant frequency using a cartogram that calculates the kurtosis of the envelope spectrum of the operation sound signal for each time window length of the short-time Fourier transform by interpreting the short-time Fourier transform that analyzes the time change of the frequency as a filter bank, and it is further preferable to use a high-speed cartogram that speeds up calculations based on a dendritic multi-rate filter bank structure composed of filters of various passbands as this cartogram.

[0016] In the second resonant frequency estimation step, it is preferable to use fast spectral correlation represented by the following equation 1 as the spectral correlation. TIFF2025033132000002.tif141170

[0017] In addition, in the bearing abnormality diagnosis method according to the present invention, an optimal bandpass filter application step of applying to the operation sound signal a bandpass filter having a center frequency corresponding to the resonant frequency deemed to match and an optimal bandwidth set according to the S / N ratio of the harmonic components centered on the center frequency, when the abnormality is determined in the abnormality determination step; an optimal bandwidth envelope spectrum calculation step of calculating an envelope spectrum of the operation sound signal to which a bandpass filter having an optimal bandwidth has been applied in the optimal bandpass filter application step; a mixing frequency amplitude value acquisition step of acquiring an amplitude value of a mixing frequency by performing frequency analysis on the envelope spectrum calculated in the optimal bandwidth envelope spectrum calculation step; a quantitative diagnosis step of estimating a degree of abnormality of the rolling bearing from the magnitude of the amplitude value of the mixed frequency acquired in the mixed frequency amplitude value acquisition step, and performing a quantitative diagnosis; Further includes:

[0018] In this bearing abnormality diagnosis method, in the optimal bandpass filter application step, a bandpass filter having an optimal bandwidth set according to the S / N ratio of the harmonic components is applied to the operation sound signal, in the optimal bandwidth envelope spectrum calculation step, an envelope spectrum of the operation sound signal to which the bandpass filter having the optimal bandwidth is applied is calculated, and in the mixed frequency amplitude value acquisition step, the envelope spectrum calculated in the optimal bandwidth envelope spectrum calculation step is frequency analyzed to acquire the amplitude value of the mixed frequency. Then, in the quantitative diagnosis step, the degree of abnormality of the bearing is estimated from the magnitude of the amplitude value. According to this bearing abnormality diagnosis method, by using a bandpass filter having an optimal bandwidth set according to the S / N ratio, it is possible to estimate the abnormality of the bearing according to the purpose, such as when it is desired to identify the location of damage or when it is desired to identify the type of damage. Furthermore, according to this bearing abnormality diagnosis method, since the greater the degree of damage, the greater the amplitude value, it is possible to estimate the degree of abnormality of the bearing from the magnitude of the amplitude value, and therefore it is possible to quantitatively diagnose the degree of abnormality of the bearing.

[0019] In the quantitative diagnosis step, it is preferable to perform quantitative diagnosis by focusing only on the first harmonic component of the mixed frequency. The first harmonic component is always included regardless of the bandwidth determined as the optimal bandwidth, so that a fair comparison can be made.

[0020] The computer program for diagnosing a bearing abnormality according to the present invention is installed in a computer device to which an audio signal acquisition device is connected and causes the computer device to diagnose abnormalities caused by damage to a rolling bearing, and is characterized in that it causes the computer device to execute the above-mentioned bearing abnormality diagnosis method of the present invention.

[0021] According to this computer program for bearing abnormality diagnosis, a computer device to which an audio signal acquisition device is connected can be caused to execute the above-mentioned bearing abnormality diagnosis method, thereby obtaining the same functions and effects as the above-mentioned bearing abnormality diagnosis method. Effect of the Invention

[0022] The bearing abnormality diagnosis method of the present invention enables non-contact and rapid measurement, making it possible to detect bearing abnormalities even when the S / N ratio of the bearing damage signal is low or when bearing damage is in the early stages. Furthermore, the computer program for diagnosing a bearing abnormality according to the present invention can cause a computer device to perform non-contact and rapid measurements, enabling the computer device to detect a bearing abnormality even when the S / N ratio of the bearing damage signal is low or when bearing damage is in the early stages. [Brief description of the drawings]

[0023] [Figure 1] FIG. 1 is a diagram showing a procedure of envelope analysis. [Diagram 2] FIG. 1 illustrates a problem in envelope analysis. [Diagram 3] FIG. 13 is a diagram illustrating the effectiveness of pre-processing using a band-pass filter. [Figure 4] 3 is a flowchart showing the procedure of a bearing abnormality diagnosis method according to the embodiment. [Diagram 5] FIG. 13 is a diagram for explaining estimation of a resonance frequency using a kurtosis index. [Figure 6] FIG. 11 is a diagram for explaining an envelope spectrum in the first resonant frequency estimation step. [Figure 7] FIG. 4 is a diagram showing a procedure for estimating a resonant frequency in a first resonant frequency estimation step. [Figure 8] FIG. 13 is a diagram showing an example of a result display using a method that uses a high-speed cartogram in the first resonant frequency estimation step. [Figure 9] FIG. 11 is a diagram showing an example of a result display by an evaluation method using fast spectrum correlation in the first resonant frequency estimation step. [Figure 10] FIG. 13 is a diagram for explaining setting of a bandwidth with a focus on the S / N ratio. [Figure 11] FIG. 1 is a diagram showing the relationship between the signal-to-noise ratio and the bandwidth. [Figure 12]10 is a flowchart showing a procedure for setting an optimal bandwidth. [Figure 13] FIG. 13 is a diagram showing the relationship between the amplitude value of the mixed frequency and the degree of damage. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0024] Below, a bearing abnormality diagnosis method as one embodiment to which the present invention is applied will be described with reference to the drawings, but first, damage to the bearing that is the subject of abnormality diagnosis, envelope analysis which is a method basically used in this invention to analyze the mixed frequencies caused by that damage, and preprocessing using a bandpass filter to clearly detect the envelope (also called an envelope curve) in the envelope analysis will be described, and then the bearing abnormality diagnosis method of the embodiment will be described. Note that each figure does not necessarily strictly reflect the actual state or all of the procedures.

[0025] (Bearing damage) A rolling bearing is mainly composed of four parts: an inner ring, an outer ring, a cage, and a rolling element. When damage occurs in any of these four parts, an impact occurs each time the rolling element comes into contact with the damaged part, and vibrations of the resonant frequency components of the machine housing are excited. This impact occurs at regular time intervals if the rotation speed does not change. This time interval period or frequency is called the mixing frequency in this specification. The mixing frequency is determined to be the frequency corresponding to the damaged part, specifically, four types are determined: the fundamental cage frequency (ETF), the ball rotation frequency (BSF), the ball passing frequency of the outer ring raceway surface (BPFO), and the ball passing frequency of the inner ring raceway surface (BPFI). Theoretically, the mixing frequency can be calculated from the specifications and rotation speed of the bearing. However, when the operating sound signal (time series signal) of a device in which a bearing is actually installed is actually acquired, it is difficult to confirm the periodic transient signal of this mixing frequency from the operating sound signal. The reason for this is that the periodic transient signal due to bearing damage is buried in machine operation sounds, such as motor operation sounds, and background noise. In this case, in order to check for periodic transient signals of mixed frequencies, it is effective to perform envelope analysis on the operation sound signal.

[0026] (Envelope analysis and preprocessing using bandpass filters) FIG. 1 shows the procedure of envelope analysis, FIG. 2 shows the problem, and FIG. 3 shows the effectiveness of pre-processing using a bandpass filter to solve the problem. In the abnormality diagnosis of damaged bearings, there is an envelope analysis method that focuses on the periodicity of the bearing damage signal. In the envelope analysis, as shown in FIG. 1, first, a time series signal of the bearing is acquired (ST11). Next, in order to extract only the periodic information of the bearing damage signal, the envelope of the obtained vibration signal is calculated (ST12). Finally, the envelope is subjected to frequency analysis to obtain an envelope spectrum (ST13). The envelope analysis is an analysis method that assumes that the bearing damage signal can be detected by the envelope, but as shown in FIG. 2, it is difficult to detect the mixed frequency under conditions of a small S / N ratio, such as when the degree of bearing damage is small or when the noise during operation or background noise is large. That is, for example, when the bearing damage level is in the final stage, the S / N ratio is large and there is a high possibility that the mixed frequency can be detected in the envelope spectrum, but when the bearing damage level is in the early stage, the S / N ratio is small and it is often the case that the resonant frequency cannot be detected in the envelope spectrum. In such cases, it is effective to perform the envelope analysis after increasing the S / N ratio. Specifically, as shown in Fig. 3, as a preprocessing step before calculating the envelope (ST12), a bandpass filter with a determined center frequency and bandwidth is prepared (ST14), and the bandpass filter is applied to the bearing time series signal (ST15), whereby the appropriate frequency band is extracted by the bandpass filter and the S / N ratio is increased.

[0027] (Bearing Abnormality Diagnosis Method) Fig. 4 is a flow chart showing the procedure of the bearing abnormality diagnosis method of the embodiment, Fig. 5 is a diagram for explaining the estimation of the resonance frequency by the kurtosis index, Fig. 6 is a diagram for explaining the envelope spectrum in the first resonance frequency estimation step ST2, Fig. 7 is a diagram showing the procedure of estimating the resonance frequency in the first resonance frequency estimation step, Fig. 8 is a diagram showing an example of the result display by the method using the high-speed cartogram, Fig. 9 is a diagram showing an example of the result display by the evaluation method using the high-speed spectrum correlation, Fig. 10 is a diagram for explaining the setting of the bandwidth focusing on the S / N ratio, Fig. 11 is a diagram showing the relationship between the S / N ratio and the bandwidth, Fig. 12 is a flow chart showing the procedure for setting the optimal bandwidth, and Fig. 13 is a diagram showing the relationship between the amplitude value of the mixed frequency and the degree of damage. The bearing abnormality diagnosis method according to the present invention will be described with reference to these figures.

[0028] As shown in FIG. 4, the bearing abnormality diagnosis method of the embodiment sequentially executes an operation sound acquisition step ST1, a first resonance frequency estimation step ST2, a second resonance frequency estimation step ST3, an abnormality determination step ST4, an optimal bandpass filter application step ST5, an optimal bandwidth envelope spectrum calculation step ST6, a mixed frequency amplitude value acquisition step ST7, and a quantitative diagnosis step ST8 to diagnose an abnormality due to damage to the bearing.

[0029] In the operation sound acquisition step ST1, an operation sound signal of the bearing is acquired using a sound collector. In the bearing abnormality diagnosis method of the embodiment, the operation sound signal acquired in the operation sound acquisition step ST1, i.e., a time-series signal that can be acquired from the bearing in a non-contact manner, is analyzed in the following steps to perform abnormality diagnosis of the bearing.

[0030] In the first resonance frequency estimation step ST2, the resonance frequency is estimated by the spectrum kurtosis. Specifically, the operation sound signal acquired in the operation sound acquisition step ST1 is divided by a filter bank arranged without gaps from low to high bands using band-pass filters of a predetermined band width, and the kurtosis of the envelope spectrum of each narrow band operation sound signal is calculated, and the resonance frequency is estimated using the kurtosis index with the largest kurtosis among the calculated kurtosis. At this time, the short-time Fourier transform (hereinafter referred to as "STFT") that analyzes the time change of the frequency is interpreted as a filter bank, and the resonance frequency is estimated using a cartogram that calculates the kurtosis of the envelope spectrum of the operation sound signal for each time window length of the short-time Fourier transform. As this cartogram, a high-speed cartogram that speeds up calculations is used based on a tree-like multi-rate filter bank structure composed of filters of various pass bands. The method of estimating the resonance frequency by the kurtosis index will be described in detail below.

[0031] Kurtosis is defined as follows: TIFF2025033132000003.tif38170

[0032] The method of estimating the resonant frequency by the kurtosis index is based on the fact that, as shown in FIG. 5, a time series signal is divided into narrow bands (divided by time window) by a filter bank arranged without gaps from low to high frequencies by band-pass filters of a predetermined bandwidth (Bw in FIG. 5), and when the time series signals of each narrow band are compared, the resonant frequency is likely to be included in the narrow band (middle band in FIG. 5) with a large amplitude that is considered to have a large kurtosis. However, as shown in the lower band in FIG. 6, even in a band that does not include a resonant frequency, the amplitude may appear large due to the influence of non-periodic noise. Therefore, as shown in FIG. 6, by calculating the envelope spectrum of the time series signal of each band, periodic signals are made to react strongly, and the influence of non-periodic noise can be removed. For this reason, in the first resonant frequency estimation step ST2, the kurtosis of the envelope spectrum of the operation sound signal is calculated. The kurtosis of the envelope spectrum of a time series signal such as an operation sound signal is calculated by STFT, which analyzes the time change of frequency by performing spectrum analysis while sliding the time window.

[0033] In the method of estimating the resonant frequency by the kurtosis index, it is preferable to develop a method using a cartogram when estimating the resonant frequency, as shown in FIG. 7. The cartogram is a representation of the spectral kurtosis on the (frequency / frequency resolution) plane, and is a process in which the STFT is interpreted as a filter bank. The cartogram is a method of calculating the spectral kurtosis for each time window length of the STFT by using the interpretation of the STFT as a filter bank. As shown in FIG. 7, the method of using the cartogram when estimating the resonant frequency acquires a time series signal (ST21), divides it into narrowband time series signals using a multirate filter bank (ST22), performs envelope analysis on each narrowband time series signal (ST23), calculates the kurtosis of all envelope spectra (ST24), expresses a cartogram from the calculated kurtosis (ST25), and selects a band with a large kurtosis by comparing the kurtosis (ST26).

[0034] Here, in the method using the cartogram, the frequency resolution is investigated in detail, so the calculation cost tends to be high. For this reason, in the first resonance frequency estimation step ST2, a high-speed cartogram is used to speed up the calculation of the cartogram based on a dendritic multi-rate filter bank structure composed of filters of various passbands. In the method of estimating the resonance frequency using the high-speed cartogram in the first resonance frequency estimation step ST2, the kurtosis of two bands having similar bandwidths is compared and the band with the larger kurtosis is selected. In this case, the band is gradually narrowed from a wide bandwidth to a narrow bandwidth, and this selection is repeated until finally the bandwidth is very narrow. As shown in FIG. 8 (from top to bottom in FIG. 8), the bandwidth to be compared gradually narrows, and the resonance frequency can be estimated as the frequency that is finally left.

[0035] In the second resonance frequency estimation step ST3, the resonance frequency is estimated from the operation sound signal acquired in the operation sound acquisition step ST1 using a spectral correlation that expresses the operation sound signal as a mixture frequency and a vibration frequency distribution. At this time, it is preferable to use a high-speed spectral correlation that enables high-speed calculation of the spectral correlation. The method of estimating the resonance frequency using this spectral correlation will be described in detail below.

[0036] Spectral correlation is a frequency expression of the autocorrelation function and can be expressed as follows: TIFF2025033132000004.tif60170

[0037] The estimation of the spectral correlation in the signal processing is performed based on the above formula 4, and a method based on STFT that expresses the signal as a mixed frequency-vibration frequency distribution is used. In the STFT-based spectral correlation, first, a time-frequency analysis of the signal is performed, and then correlation processing in the frequency domain is performed according to the above formula 4.

[0038] Here, an estimation method based on STFT is Averaged Cyclic Periodogram (hereinafter, referred to as "ACP"). ACP is expressed by the following Equation 2, which is called scanning vector correlation. TIFF2025033132000005.tif90170

[0039] Also, as an estimation method based on STFT, there is Cyclic Modulation Spectrum (hereinafter referred to as "CMS"), which is different from spectral correlation but has the same purpose as spectral correlation in that it analyzes a signal in the frequency-spectral frequency representation of the envelope. CMS performs frequency analysis in the time direction on the power of a signal obtained by time-frequency analysis to obtain the frequency-spectral frequency representation of the envelope, and effective calculation is performed by performing a discrete Fourier transform (DFT) on the spectrogram in the time direction. ACP has high estimation accuracy but tends to have high calculation costs, while CMS has low calculation costs but tends to have low estimation accuracy. Therefore, in the second resonant frequency estimation step ST3, high-speed spectral correlation is used, taking advantage of the advantages of ACP and CMS.

[0040] Fast spectral correlation is a method that extends the concept of CMS to approach ideal spectral correlation while maintaining low computational cost, and can be expressed as the following equation 1. Fast spectral correlation can reduce computational cost while maintaining the same statistical performance as ACP. TIFF2025033132000006.tif141170

[0041] In the second resonance frequency estimation step ST3, when estimating the resonance frequency using fast spectral correlation, evaluation is performed using spectral coherence to provide greater sensitivity to periodicity. Spectral coherence is a function that represents the strength of correlation in the frequency domain, and is given by the following equation 5. Spectral coherence normalizes the spectral correlation from 0 to 1. In addition, it has the property of emphasizing periodic but low-level signals, making it possible to provide greater sensitivity to periodicity. TIFF2025033132000007.tif31170

[0042] In the second resonant frequency estimation step ST3, the calculated spectral coherence is displayed on the envelope frequency-spectral frequency plane, color-coded according to the strength of correlation, as shown in Fig. 9 (Fig. 9 is a monochrome diagram and is not color-coded). In the second resonant frequency estimation step ST3, the frequency of the envelope at the point of strong correlation on this display, i.e., at the point of large spectral coherence, is estimated as the resonant frequency.

[0043] In the abnormality determination step ST4, the resonant frequencies estimated in the first resonant frequency estimation step ST2 and the second resonant frequency estimation step ST3 are compared, and if they are deemed to match, it is determined to be abnormal, and if they are not deemed to match, it is determined to be normal. Here, the estimated values ​​of the resonant frequency in the first resonant frequency estimation step ST2 and the second resonant frequency estimation step ST3 are affected by various variation factors such as variation in the measurement equipment, variation in measurement accuracy, and variation in the measurement environment, so that in the abnormality determination step ST4, it is not required that the estimated values ​​strictly match, and the conditions for matching are appropriately set taking these variations into consideration. For example, if the resonant frequency can be estimated by both, the conditions for matching may be set to within a certain percentage of either estimated value, within a certain percentage of the average of the estimated values ​​of both, or the range of difference, etc.

[0044] In optimal bandpass filter application step ST5, if an abnormality is determined in abnormality determination step ST4, a bandpass filter having a center frequency corresponding to the resonant frequency that is deemed to match and an optimal bandwidth set according to the S / N ratio of the harmonic components centered on the center frequency is applied to the operation sound signal.

[0045] Here, the relationship between the bandwidth and the S / N ratio will be explained. As shown in Figs. 10 and 11, when the bandwidth of the bandpass filter is narrow, the S / N ratio is high and it is easy to detect the mixed frequency, but the calculated envelope spectrum only shows the information of the mixed frequency period. On the other hand, when the bandwidth of the bandpass filter is wide, the S / N ratio is low and it is difficult to detect the mixed frequency, but the calculated envelope spectrum shows not only the information of the mixed frequency period but also the information of the waveform shape. In other words, it is considered that the harmonic components contain information about the waveform shape of the bearing damage signal and the fluctuation of the impact time. If you want to identify the location of the bearing damage, you should set the S / N ratio threshold high, and if you want to identify the type of bearing damage, you should set the S / N ratio threshold low.

[0046] When diagnosing a bearing, it is preferable to select an optimum bandwidth that can detect harmonic components in order to obtain as much information as possible from the bearing damage signal. For this reason, it is preferable to extend the optimum bandwidth to a width where a peak is observed in the high frequency components.

[0047] As shown in FIG. 12, the specific procedure for setting the optimum bandwidth for diagnosing a bearing is to first apply a bandpass filter with a bandwidth shown in the following equation 6 to the operation sound signal to calculate the envelope spectrum (ST31). TIFF2025033132000008.tif23170 Next, the S / N ratio in the calculated envelope spectrum is calculated according to Equation 7 (ST32). TIFF2025033132000009.tif25170Note that the analysis window used to calculate the S / N ratio (ST32) slides in accordance with the bandwidth while maintaining the analysis window length, as shown on the envelope spectrum waveform in Figure 10. This makes it possible to select a bandwidth where a peak can be confirmed in the amplitude value of the nth harmonic, and to determine a bandwidth that does not lose information on the harmonic components. Next, if the S / N ratio exceeds a predetermined threshold, n is updated and the process returns to the first step (ST31) (ST33).On the other hand, if the S / N ratio does not exceed the predetermined threshold, the optimal bandwidth is determined to be the bandwidth shown in the following formula 8 (ST34). TIFF2025033132000010.tif23170

[0048] In optimum bandwidth envelope spectrum calculation step ST6, as shown in FIG. 11, the envelope spectrum of the operation sound signal to which the bandpass filter having the optimum bandwidth has been applied in optimum bandpass filter application step ST5 is calculated.

[0049] In the contamination frequency amplitude value acquisition step ST7, the envelope spectrum calculated in the optimum bandwidth envelope spectrum calculation step ST6 is frequency analyzed to acquire the amplitude value of the contamination frequency.

[0050] In the quantitative diagnosis step ST8, the degree of abnormality of the bearing is estimated from the magnitude of the amplitude value of the mixing frequency acquired in the mixing frequency amplitude value acquisition step ST7, and quantitative diagnosis is performed. More specifically, as shown in FIG. 13, the relationship between the amplitude value of the mixing frequency and the degree of damage is such that the greater the degree of damage, the greater the amplitude value of the mixing frequency. That is, in FIG. 13, the degree of damage increases from (a) to (f), but the amplitude value of the mixing frequency increases from (a) to (f). This is also true when focusing only on the first harmonic component of the mixing frequency. In the quantitative diagnosis step ST8, it is preferable to estimate the degree of abnormality of the bearing by focusing only on the magnitude of the amplitude value of the first harmonic component. This is because the first harmonic component is always included regardless of the bandwidth determined, and a fair comparison can be made.

[0051] As described above, in the bearing abnormality diagnosis method according to one embodiment of the present invention, in the operation sound acquisition step ST1, an abnormality is diagnosed from the operation sound signal of the bearing acquired in a non-contact manner. In the first resonance frequency estimation step ST2 and the second resonance frequency estimation step ST3, the resonance frequency is estimated using different analysis methods, and it is determined that an abnormality has occurred if the two estimation results can be regarded as equivalent. If an abnormality has occurred, the abnormal state of the bearing is estimated in the optimal bandpass filter application step ST5 and the optimal bandwidth envelope spectrum calculation step ST6. Furthermore, in the mixed frequency amplitude value acquisition step ST7 and the quantitative diagnosis step ST8, the degree of damage to the bearing is quantitatively estimated.

[0052] (Bearing abnormality diagnosis system) The above-mentioned bearing abnormality diagnosis method can be reflected in a bearing abnormality diagnosis system for diagnosing bearing abnormalities. One example of the abnormality diagnosis system is configured as a computer device to which an audio signal acquisition device is connected. By installing a bearing abnormality diagnosis computer program for executing various processes for carrying out the above-mentioned bearing abnormality diagnosis method to which the present invention is applied, the abnormality diagnosis system can be constructed.

[0053] That is, the computer program for abnormality diagnosis first causes the computer device to execute an operation sound acquisition process for acquiring an operation sound signal of a rolling bearing. Next, the computer device executes a first resonance frequency estimation process for calculating the kurtosis of the envelope spectrum of the operation sound signal of each band obtained by dividing the operation sound signal acquired by the operation sound acquisition process using a filter bank arranged without gaps from low to high frequencies using bandpass filters of a predetermined bandwidth, and estimating the resonance frequency using the kurtosis index having the largest kurtosis among the calculated kurtosis, and a second resonance frequency estimation process for estimating the resonance frequency from the operation sound signal acquired by the operation sound acquisition process using a spectrum correlation that expresses the operation sound signal by a mixing frequency and a vibration frequency distribution. Next, the computer device executes an abnormality determination process for comparing the respective resonance frequencies estimated by the first and second resonance frequency estimation processes, and determining that an abnormality exists if the frequencies are deemed to match, and determining that an abnormality exists if the frequencies are not deemed to match. Next, when the abnormality is determined in the abnormality determination process, an optimal bandpass filter application process is executed in which a bandpass filter having a center frequency corresponding to the resonant frequency deemed to match and an optimal bandwidth set according to the S / N ratio of the harmonic components centered on the center frequency is applied to the operation sound signal. Next, an optimal bandwidth envelope spectrum calculation process is executed in which an envelope spectrum of the operation sound signal to which the bandpass filter having the optimal bandwidth has been applied in the optimal bandpass filter application process is calculated. Next, a mixing frequency amplitude value acquisition process is executed in which the envelope spectrum calculated in the optimal bandwidth envelope spectrum calculation process is frequency analyzed to acquire the amplitude value of the mixing frequency. Next, a quantitative diagnosis process is executed in which the degree of abnormality of the rolling bearing is estimated from the magnitude of the amplitude value of the mixing frequency acquired in the mixing frequency amplitude value acquisition process, and a quantitative diagnosis is performed.

[0054] In the first resonant frequency estimation process, it is preferable to have the computer device estimate the resonant frequency using a cartogram that interprets a short-time Fourier transform that analyzes a time change in frequency as a filter bank and calculates the kurtosis of the envelope spectrum of the operation sound signal for each time window length of the short-time Fourier transform. In this case, it is preferable to use a high-speed cartogram that speeds up calculations based on a dendritic multi-rate filter bank structure composed of filters of various passbands as the cartogram.

[0055] In the second resonant frequency estimation process, it is preferable to use fast spectral correlation as the spectral correlation.

[0056] In the quantitative diagnosis process, it is preferable to have the computer device focus only on the first harmonic component of the interference frequency to perform the quantitative diagnosis.

[0057] (Action and effect) In the bearing abnormality diagnosis method according to the embodiment of the present invention, the signal used for abnormality diagnosis is the operation sound signal acquired in the operation sound acquisition step ST1, so that non-contact and rapid measurement is possible. In addition, in the abnormality judgment step ST4, the logical product of the respective resonance frequencies estimated in the first resonance frequency estimation step ST2 and the second resonance frequency estimation step ST3 is taken to judge abnormality, so that the reliability of the diagnosis result is high. That is, in the first resonance frequency estimation step ST2, the resonance frequency is estimated from the calculated value as a relative quantity using the kurtosis index, and in the second resonance frequency estimation step ST3, the resonance frequency is estimated from the calculated value as an absolute quantity using the spectrum correlation, so that the resonance frequency is identified when similar results are obtained even with methods of completely different natures, and the identification accuracy is high. In addition, in this bearing abnormality diagnosis method, in the first resonance frequency estimation step ST2, the kurtosis of the envelope spectrum of the operation sound signal of each band divided by a filter bank arranged without gaps from low to high frequencies by a bandpass filter of a predetermined bandwidth is calculated. The envelope of the operating sound signal preprocessed with a bandpass filter eliminates the influence of non-periodic noise components and clearly detects periodic noise components. As a result, according to this bearing abnormality diagnosis method, even if the S / N ratio of the bearing damage signal is low or the bearing damage is in the early stages, the non-stationary waveform of the bearing damage can be extracted using frequency analysis techniques, and bearing abnormalities can be detected with improved reliability.

[0058] In addition, in this bearing abnormality diagnosis method, in an optimal bandpass filter application step ST5, a bandpass filter having an optimal bandwidth set according to the S / N ratio of the harmonic components is applied to the operation sound signal, in an optimal bandwidth envelope spectrum calculation step ST6, an envelope spectrum of the operation sound signal to which the bandpass filter having the optimal bandwidth is applied is calculated, and in a mixed frequency amplitude value acquisition step ST7, the envelope spectrum calculated in the optimal bandwidth envelope spectrum calculation step ST6 is frequency analyzed to acquire the amplitude value of the mixed frequency. Then, in a quantitative diagnosis step ST8, the degree of abnormality of the bearing is estimated from the magnitude of the amplitude value. According to this bearing abnormality diagnosis method, by using a bandpass filter having an optimal bandwidth set according to the S / N ratio, it is possible to estimate the abnormality of the bearing according to the purpose, such as when it is desired to identify the location of damage or when it is desired to identify the type of damage. Furthermore, according to this bearing abnormality diagnosis method, since the greater the degree of damage, the greater the amplitude value, it is possible to estimate the degree of abnormality of the bearing from the magnitude of the amplitude value, and therefore it is possible to quantitatively diagnose the degree of abnormality of the bearing.

[0059] According to the computer program for diagnosing a bearing abnormality of the embodiment to which the present invention is applied, a computer device to which an audio signal acquisition device is connected can be caused to execute the bearing abnormality diagnosis method of the embodiment, thereby obtaining the same functions and effects as the bearing abnormality diagnosis method of the embodiment.

[0060] [Other formats] Although the present invention has been described based on the above embodiment, the present invention is not limited to the above embodiment. The present invention can be embodied in various forms without departing from the spirit of the present invention, and for example, the following modifications are also possible.

[0061] (1) The specific methods, formula formats, illustrations of waveforms and analysis results, etc. described in the above embodiments are merely examples and may be changed without impairing the effects of the present invention.

[0062] (2) In the above embodiment, the first resonance frequency estimation step ST2 uses a fast cartogram, and the second resonance frequency estimation step ST3 uses fast spectral correlation, but the present invention is not limited to this. The first resonance frequency estimation step ST2 may estimate the resonance frequency from the kurtosis index of the envelope spectrum of the operation sound signal without using a fast cartogram. Also, the second resonance frequency estimation step ST3 may estimate the resonance frequency by spectral correlation without using fast spectral correlation. EXAMPLES

[0063] As an example, two samples were prepared, one each of a bearing that was actually operating normally and a bearing that actually showed an abnormality, and these were subjected to the operation sound acquisition step ST1 to the abnormality determination step ST4 of the bearing abnormality diagnosis method of the embodiment, and a determination was made, obtaining the results shown in Table 1 below. Note that in the example, in the abnormality determination step ST4, the condition under which the estimated value in the first resonance frequency estimation step ST2 and the estimated value in the second resonance frequency estimation step ST3 can be considered to match is set as follows: "When the resonance frequency can be estimated by both, if the difference is within 10% of the higher estimated resonance frequency, they are considered to match." [Table 1]

[0064] In the embodiment, as shown in Table 1, for two normal samples, the first resonance frequency estimation step ST2 estimated the resonance frequency (one sample was 6400 Hz, the other sample was 1700 Hz), but in the second resonance frequency estimation step ST3, a peak that could be estimated as the resonance frequency at the mixing frequency of 130 Hz could not be confirmed in the cartogram, and the resonance frequency could not be estimated. Therefore, for the two normal samples, the estimated value in the first resonance frequency estimation step ST2 and the estimated value in the second resonance frequency estimation step ST3 could not be regarded as being the same, and the two normal samples were judged to be normal in the abnormality judgment step ST4.

[0065] On the other hand, for the two abnormal samples, the resonant frequency (2600 Hz for both samples) was estimated in the first resonant frequency estimation step ST2, and a peak was confirmed in the cartogram at the contamination frequency of 130 Hz, and the resonant frequency (2500 Hz for both samples) was estimated in the second resonant frequency estimation step ST3. The difference between the two estimated values ​​was 100 Hz, which was within 10% (3.8%) of the higher resonant frequency of 2600 Hz, so they were deemed to match and were judged to be abnormal in the abnormality judgment step ST4.

[0066] In the examples, it was confirmed that bearing abnormalities could be correctly determined. [Explanation of symbols]

[0067] ST1: Operation sound acquisition step, ST2: First resonance frequency estimation step, ST3: Second resonance frequency estimation step, ST4: Abnormality determination step, ST5: Optimal band pass filter application step, ST6: Optimal bandwidth envelope spectrum calculation step, ST7: Contamination frequency amplitude value acquisition step, ST8: Quantitative diagnosis step

Claims

1. A method for diagnosing an abnormality in a rolling bearing, which diagnoses an abnormality due to damage to the rolling bearing, comprising: an operation sound acquisition step of acquiring an operation sound signal of the rolling bearing; a first resonant frequency estimation step of calculating the kurtosis of an envelope spectrum of the operation sound signal in each band obtained by dividing the operation sound signal acquired in the operation sound acquisition step by a filter bank arranged without gaps from low to high frequencies using band-pass filters of a predetermined bandwidth, and estimating a resonant frequency using a kurtosis index having the largest kurtosis among the calculated kurtosis; a second resonance frequency estimation step of estimating a resonance frequency from the operation sound signal acquired in the operation sound acquisition step by using a spectral correlation in which the operation sound signal is expressed by a mixing frequency and a vibration frequency distribution; an abnormality determination step of comparing the respective resonant frequencies estimated in the first and second resonant frequency estimation steps, and determining an abnormality when the resonant frequencies are deemed to match, and determining a normality when the resonant frequencies are not deemed to match; A method for diagnosing an abnormality in a rolling bearing, comprising:

2. The method for diagnosing an abnormality in a rolling bearing according to claim 1, The first resonant frequency estimation step estimates the resonant frequency using a cartogram that calculates the kurtosis of the envelope spectrum of the operation sound signal for each time window length of the short-time Fourier transform by interpreting the short-time Fourier transform, which analyzes a change in frequency over time, as the filter bank.

3. The method for diagnosing an abnormality in a rolling bearing according to claim 2, The method for diagnosing abnormalities in rolling bearings uses, as the cartogram, a high-speed cartogram that speeds up calculations based on a dendritic multi-rate filter bank structure composed of filters of various passbands.

4. The method for diagnosing an abnormality in a rolling bearing according to claim 1, The method for diagnosing an abnormality in a rolling bearing, wherein the second resonant frequency estimation step uses a high-speed spectral correlation represented by the following equation 1 as the spectral correlation.

5. The method for diagnosing an abnormality in a rolling bearing according to any one of claims 1 to 4, an optimal bandpass filter application step of applying to the operation sound signal a bandpass filter having a center frequency corresponding to the resonant frequency that is deemed to coincide and an optimal bandwidth set according to an S / N ratio of harmonic components centered on the center frequency, when the abnormality is determined in the abnormality determination step; an optimal bandwidth envelope spectrum calculation step of calculating an envelope spectrum of the operation sound signal to which a bandpass filter having the optimal bandwidth has been applied in the optimal bandpass filter application step; a mixing frequency amplitude value acquisition step of acquiring an amplitude value of a mixing frequency by performing frequency analysis on the envelope spectrum calculated in the optimum bandwidth envelope spectrum calculation step; a quantitative diagnosis step of estimating a degree of abnormality of the rolling bearing from the magnitude of the amplitude value of the mixed frequency acquired in the mixed frequency amplitude value acquisition step, and performing a quantitative diagnosis; The method for diagnosing an abnormality in a rolling bearing further comprises:

6. The method for diagnosing an abnormality in a rolling bearing according to claim 5, In the quantitative diagnosis step, the quantitative diagnosis is performed by focusing only on a first harmonic component of the mixed frequency.

7. A computer program for diagnosing abnormalities in rolling bearings, the computer program being installed in a computer device to which an audio signal acquisition device is connected and causing the computer device to diagnose abnormalities caused by damage to a rolling bearing, The computer device includes: an operation sound acquisition process for acquiring an operation sound signal of the rolling bearing; a first resonant frequency estimation process for calculating the kurtosis of an envelope spectrum of the operation sound signal in each band obtained by dividing the operation sound signal acquired in the operation sound acquisition process using a filter bank in which band-pass filters of a predetermined bandwidth are used to arrange the bands without gaps from low to high frequencies, and estimating a resonant frequency using a kurtosis index having the largest kurtosis among the calculated kurtosis; a second resonant frequency estimation process for estimating a resonant frequency from the operation sound signal acquired in the operation sound acquisition process by using a spectral correlation in which the operation sound signal is expressed by a mixing frequency and a vibration frequency distribution; an abnormality determination process for comparing the resonant frequencies estimated in the first and second resonant frequency estimation processes, determining that an abnormality has occurred if the resonant frequencies are deemed to match, and determining that an abnormality has occurred if the resonant frequencies are not deemed to match; A computer program for diagnosing abnormalities in a rolling bearing, comprising:

8. 8. The rolling bearing abnormality diagnosis computer program according to claim 7, The computer program for abnormality diagnosis of a rolling bearing causes the computer device, in the first resonant frequency estimation process, to estimate the resonant frequency using a cartogram that interprets a short-time Fourier transform that analyzes a time change of frequency as the filter bank and calculates the kurtosis of the envelope spectrum of the operation sound signal for each time window length of the short-time Fourier transform.

9. 9. The rolling bearing abnormality diagnosis computer program according to claim 8, A computer program for diagnosing abnormalities in rolling bearings, which uses, as the cartogram, a high-speed cartogram that speeds up calculations based on a dendritic multi-rate filter bank structure composed of filters of various passbands.

10. 8. The rolling bearing abnormality diagnosis computer program according to claim 7, A computer program for diagnosing an abnormality in a rolling bearing, the computer program comprising: a first resonant frequency estimating process that estimates a resonant frequency of a rolling bearing using a high-speed spectral correlation represented by the following equation 1 as the spectral correlation.

11. The computer program for diagnosing an abnormality in a rolling bearing according to any one of claims 7 to 10, The computer device further comprises: an optimal bandpass filter application process for applying to the operation sound signal a bandpass filter having a center frequency corresponding to the resonant frequency that is deemed to match and an optimal bandwidth set according to an S / N ratio of harmonic components centered on the center frequency, when the abnormality is determined in the abnormality determination process; an optimal bandwidth envelope spectrum calculation process for calculating an envelope spectrum of the operation sound signal to which a bandpass filter having the optimal bandwidth has been applied in the optimal bandpass filter application process; a mixing frequency amplitude value acquisition process for acquiring an amplitude value of a mixing frequency by subjecting the envelope spectrum calculated in the optimal bandwidth envelope spectrum calculation process to frequency analysis; a quantitative diagnosis process for estimating a degree of abnormality of the rolling bearing from the magnitude of the amplitude value of the mixed frequency acquired in the mixed frequency amplitude value acquisition process, and performing a quantitative diagnosis; A computer program for diagnosing abnormalities in rolling bearings.

12. 12. The computer program for diagnosing an abnormality in a rolling bearing according to claim 11, A computer program for diagnosing abnormalities in rolling bearings, the computer program causing the computer device to focus only on a first harmonic component of the mixed frequency and perform the quantitative diagnosis in the quantitative diagnosis process.

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