CN-122016332-A - Method, device, equipment and readable storage medium for determining fault parts

CN122016332ACN 122016332 ACN122016332 ACN 122016332ACN-122016332-A

Abstract

A fault part determining method comprises the steps of determining a fault frequency section, carrying out wavelet packet decomposition on a vibration signal to obtain a plurality of sub-band signals, forming the sub-band signals in the fault frequency section into a fault vibration signal, carrying out empirical mode decomposition on the fault vibration signal to obtain a plurality of eigen mode functions, calculating correlation coefficients between each eigen mode function and the fault vibration signal, carrying out Hilbert transform on the eigen mode function aiming at the eigen mode function with the correlation coefficients larger than a preset coefficient, solving an envelope signal of the eigen mode function, carrying out Fourier transform on the envelope signal to obtain a second frequency domain signal, calculating a difference value between a frequency corresponding to a maximum amplitude signal in the second frequency domain signal and a characteristic frequency of each part of a power assembly, and taking the part with the difference value smaller than the preset difference value as the fault part. The application can lock the fault parts efficiently and accurately.

Inventors

Zuo Yueyun
LIU JINLONG
SHI SHOUCHUANG
LI HENG

Assignees

东风汽车集团股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260121

Claims (15)

1. A method of determining a faulty component, the method comprising: Collecting vibration signals of the power assembly in an abnormal sound period; Performing Fourier transform on the vibration signal to obtain a first frequency domain signal, and determining a fault frequency segment based on the amplitude of the first frequency domain signal; Carrying out wavelet packet decomposition on the vibration signals to obtain a plurality of sub-band signals, wherein each sub-band signal is positioned in a different frequency band, and forming the sub-band signals positioned in a fault frequency band into fault vibration signals; performing empirical mode decomposition on the fault vibration signals to obtain a plurality of eigenmode functions, and calculating a correlation coefficient between each eigenmode function and the fault vibration signals; Aiming at the eigenmode function with the correlation coefficient larger than the preset coefficient, carrying out Hilbert transformation on the eigenmode function, solving an envelope signal of the eigenmode function, and carrying out Fourier transformation on the envelope signal to obtain a second frequency domain signal; and calculating the difference value between the frequency corresponding to the maximum amplitude signal in the second frequency domain signal and the characteristic frequency of each part of the power assembly, and taking the part corresponding to the difference value smaller than the preset difference value as the fault part.
2. The method of determining a faulty component according to claim 1, wherein determining the faulty frequency segment based on the magnitude of the first frequency domain signal includes: Splitting the first frequency domain signal into a plurality of frequency bins; and aiming at each frequency interval, if the average value of the signal amplitude values in the frequency interval is larger than the preset amplitude value, taking the frequency interval as a fault frequency section.
3. The method of claim 1, wherein the correlation coefficient is a pearson correlation coefficient, and wherein calculating the correlation coefficient between each eigenmode function and the fault vibration signal comprises: the pearson correlation coefficient between each eigenmode function and the fault vibration signal is calculated by the formula one: ; wherein C is the pearson correlation coefficient, n is the total number of signal sampling points, Representing the value of each eigenmode function at the ith sample point, Representing the average of all the sampling points of each eigenmode function, For the value of the fault vibration signal at the i-th sampling point, The mean value of all sampling points of the fault vibration signal.
4. The method of determining a fault component as claimed in claim 1, wherein performing a hilbert transform on the eigenmode function and solving an envelope signal of the eigenmode function comprises: Performing Hilbert transformation on the eigenmode function to obtain orthogonal components of the eigenmode function; Combining the eigenmode function and the orthogonal component to obtain an analysis signal; And performing modulo operation on the analysis signal to obtain an envelope signal of the eigenmode function.
5. The method for determining a faulty component according to claim 1, wherein the characteristic frequencies of the component include a first-order characteristic frequency, a second-order characteristic frequency, and a third-order characteristic frequency, and the calculating a difference between the frequency corresponding to the maximum amplitude signal in the second frequency domain signal and the characteristic frequency of each component of the power assembly, and the component having the difference smaller than the preset difference as the faulty component includes: for each part, calculating the difference value between the frequency corresponding to the maximum amplitude signal in the second frequency domain signal and the first-order characteristic frequency, the second-order characteristic frequency and the third-order characteristic frequency of the part; And if any difference value is smaller than the preset difference value, taking the part as a fault part.
6. The method of determining a faulty component according to claim 5, comprising, before calculating, for each component, a difference between a frequency corresponding to the maximum amplitude signal in the second frequency domain signal and first, second, and third order characteristic frequencies of the component, respectively: For each part, calculating to obtain a first-order characteristic frequency of the part according to the basic frequency and the rotating speed of the part, wherein the rotating speed is determined based on the vehicle speed at the time of signal acquisition; and calculating the second-order characteristic frequency and the third-order characteristic frequency of the part based on the first-order characteristic frequency of the part.
7. The method of determining a faulty component according to claim 6, wherein the calculating the first-order characteristic frequency of the component according to the fundamental frequency and the rotational speed of the component includes: the first-order characteristic frequency of the part is obtained through calculation according to a formula II according to the basic frequency and the rotating speed of the part, wherein the formula II is as follows: First order characteristic frequency of component = fundamental frequency of component x (rotational speed/60).
8. The method of determining a faulty component according to claim 1, wherein the period of occurrence of abnormal sound of the powertrain is determined based on an acoustic signal collected by the microphone.
9. The method of claim 2, wherein the predetermined amplitude is a predetermined multiple of an average value of amplitude values of vibration signals of the power train in the abnormal sound free period.
10. A fault component determining device is characterized in that, the fault part determining apparatus includes: the acquisition module is used for acquiring vibration signals of the power assembly in an abnormal sound period; the first conversion module is used for carrying out Fourier transform on the vibration signal to obtain a first frequency domain signal, and determining a fault frequency segment based on the amplitude of the first frequency domain signal; The decomposition module is used for carrying out wavelet packet decomposition on the vibration signals to obtain a plurality of sub-band signals, wherein each sub-band signal is positioned in a different frequency section, and the sub-band signals positioned in the fault frequency section form a fault vibration signal; The calculation module is used for carrying out empirical mode decomposition on the fault vibration signals to obtain a plurality of eigenmode functions, and calculating the correlation coefficient between each eigenmode function and the fault vibration signals; the second conversion module is used for carrying out Hilbert transformation on the eigen mode function aiming at the eigen mode function with the correlation coefficient larger than the preset coefficient, solving an envelope signal of the eigen mode function, and carrying out Fourier transformation on the envelope signal to obtain a second frequency domain signal; And the comparison module is used for calculating the difference value between the frequency corresponding to the maximum amplitude signal in the second frequency domain signal and the characteristic frequency of each part of the power assembly, and taking the part with the difference value smaller than the preset difference value as the fault part.
11. The faulty component determining device of claim 10, wherein the determining the faulty frequency segment based on the magnitude of the first frequency domain signal is to: Splitting the first frequency domain signal into a plurality of frequency bins; and aiming at each frequency interval, if the average value of the signal amplitude values in the frequency interval is larger than the preset amplitude value, taking the frequency interval as a fault frequency section.
12. The faulty component determining device of claim 10, wherein the correlation coefficient is a pearson correlation coefficient, and the calculating the correlation coefficient between each eigenmode function and the faulty vibration signal is configured to: the pearson correlation coefficient between each eigenmode function and the fault vibration signal is calculated by the formula one: ; wherein C is the pearson correlation coefficient, n is the total number of signal sampling points, Representing the value of each eigenmode function at the ith sample point, Representing the average of all the sampling points of each eigenmode function, For the value of the fault vibration signal at the i-th sampling point, The mean value of all sampling points of the fault vibration signal.
13. The fault component determining device of claim 10, wherein the performing a hilbert transform on the eigenmode function and solving an envelope signal of the eigenmode function is for: Performing Hilbert transformation on the eigenmode function to obtain orthogonal components of the eigenmode function; Combining the eigenmode function and the orthogonal component to obtain an analysis signal; And performing modulo operation on the analysis signal to obtain an envelope signal of the eigenmode function.
14. A faulty component determining apparatus, characterized in that the faulty component determining apparatus includes a processor, a memory, and a faulty component determining program stored on the memory and executable by the processor, wherein the faulty component determining program, when executed by the processor, implements the steps of the faulty component determining method according to any one of claims 1 to 9.
15. A readable storage medium, wherein a faulty component determination program is stored on the readable storage medium, wherein the faulty component determination program, when executed by a processor, implements the steps of the faulty component determination method according to any one of claims 1 to 9.

Description

Method, device, equipment and readable storage medium for determining fault parts Technical Field The present application relates to the field of vehicle fault diagnosis technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for determining a fault component. Background The power assembly of the vehicle mainly comprises an engine/motor and a transmission system (comprising a clutch, a gearbox, a transmission shaft, a differential mechanism, a half shaft and the like), the abnormal vibration signals of all parts inside the power assembly cannot be collected in a short distance due to the high integration characteristic of the power assembly, the vibration signals can only be collected outside the power assembly, and then the abnormal sound of the power assembly can be determined by comparing the abnormal vibration signals with the normal vibration signals. After abnormal sound exists in the power assembly, normal parts are replaced to be verified to lock the fault parts, however, more parts in the power assembly are needed, the fault parts can be locked only through repeated replacement of the normal parts, and the efficiency is low. In summary, at present, after abnormal sound occurs in the power assembly, fault parts which generate abnormal sound sources cannot be locked efficiently. Disclosure of Invention The application provides a method, a device, equipment and a readable storage medium for determining fault parts, and aims to solve the technical problem that the fault parts generating abnormal sound sources cannot be locked efficiently after abnormal sound occurs in a power assembly at present. In a first aspect, an embodiment of the present application provides a method for determining a faulty component, where the method includes: Collecting vibration signals of the power assembly in an abnormal sound period; Performing Fourier transform on the vibration signal to obtain a first frequency domain signal, and determining a fault frequency segment based on the amplitude of the first frequency domain signal; Carrying out wavelet packet decomposition on the vibration signals to obtain a plurality of sub-band signals, wherein each sub-band signal is positioned in a different frequency band, and forming the sub-band signals positioned in a fault frequency band into fault vibration signals; performing empirical mode decomposition on the fault vibration signals to obtain a plurality of eigenmode functions, and calculating a correlation coefficient between each eigenmode function and the fault vibration signals; Aiming at the eigenmode function with the correlation coefficient larger than the preset coefficient, carrying out Hilbert transformation on the eigenmode function, solving an envelope signal of the eigenmode function, and carrying out Fourier transformation on the envelope signal to obtain a second frequency domain signal; and calculating the difference value between the frequency corresponding to the maximum amplitude signal in the second frequency domain signal and the characteristic frequency of each part of the power assembly, and taking the part corresponding to the difference value smaller than the preset difference value as the fault part. Optionally, the determining the fault frequency band based on the amplitude of the first frequency domain signal includes: Splitting the first frequency domain signal into a plurality of frequency bins; and aiming at each frequency interval, if the average value of the signal amplitude values in the frequency interval is larger than the preset amplitude value, taking the frequency interval as a fault frequency section. Optionally, the correlation coefficient is a pearson correlation coefficient, and calculating the correlation coefficient between each eigenmode function and the fault vibration signal includes: the pearson correlation coefficient between each eigenmode function and the fault vibration signal is calculated by the formula one: ; wherein C is the pearson correlation coefficient, n is the total number of signal sampling points, Representing the value of each eigenmode function at the ith sample point,Representing the average of all the sampling points of each eigenmode function,For the value of the fault vibration signal at the i-th sampling point,The mean value of all sampling points of the fault vibration signal. Optionally, the performing hilbert transformation on the eigenmode function and solving the envelope signal of the eigenmode function includes: Performing Hilbert transformation on the eigenmode function to obtain orthogonal components of the eigenmode function; Combining the eigenmode function and the orthogonal component to obtain an analysis signal; And performing modulo operation on the analysis signal to obtain an envelope signal of the eigenmode function. Optionally, the characteristic frequencies of the parts include a first-order characteristic frequency, a second-order characteristi