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Bearing Fault Diagnosis Method by Multilevel Spectral Segmentation Theory and Signal Fusion

ZHANG KUN 三重大学

2022.12.08

概要

Effectively identifying the health status of rolling bearings can reduce the maintenance costs of rotating mechanical components. With the development and improvement of various signal processing theories, the mode of extracting fault information from the frequency domain has gradually replaced the mode from the time domain. In this paper, by optimizing the singlelevel spectral segmentation methods such as analytical mode decomposition, frequency slice wavelet transform, empirical wavelet transform, and quaternion, the corresponding multi-level spectral segmentation method and variable tower boundary distribution diagram and feature screening index are designed. The detailed research content is as follows:

(1) An adaptive Ailinggram that uses variable spectral segmentation framework to optimize analytical mode decomposition to automatically decompose the mode information in rotating machinery signals was proposed. The framework relies on the variability of the window width and envelope estimation characteristics of order statistics filter to increase the diversity of the center frequencies and bandwidth. A novel harmonic correlation index is designed to identify the characteristics of rotating machinery faults from various levels of results, and to improve the usability in mechanical equipment fault diagnosis. The method can be applied to fault diagnosis of rotating machinery under high speed/dynamic load conditions.

(2) Fast Entrogram was proposed to segment the spectrum and accurately filtering fault information from the frequency domain. The fluctuation state of the Fourier spectrum is of key importance in distinguishing the distribution of different components in the signal at each frequency. After the Fourier transform of the spectrum is intercepted and reconstructed, the minimum points of the new sequence can separate different components in the signal. Subsequently, the frequency slice function is used to extract each frequency band to obtain

better filtering effects than the finite impulse response filter. Finally, the proposed novel correlation spectral negentropy is sensitive to periodic pulses and can be used to screen the component that contains the most fault information. The simulation results show that the proposed Fast Entrogram can effectively extract periodic pulses. It is verified by experimental signals that the method can be applied to fault diagnosis of rotating machinery under low speed/heavy load conditions.

(3) The power spectral density will be calculated and used to segment the spectrum, which can reduce the number of extreme points and the dependence on them. According to the variability of the PSD window width, a tower boundaries distribution diagram (W-Autogram) and weighted unbiased autocorrelation would be used to extract specific information is proposed. Simulation signals and experimental results verify that the proposed method can be applied to the fault diagnosis of rolling bearings in rotating machinery.

(4) In order to extract the periodic pulse information in the signal and weaken the influence of the interference signal, we proposed Harmonic spectral kurtosis which can extract the harmonic information in the envelope spectrum, quantify the periodic pulses in the signal, and suppress the influence of interference such as random pulse. The simulation signal shows that the proposed method is accurate and effective. The data of bearing inner ring, outer ring and compound faults prove that the method can be applied to bearing fault diagnosis.

(5) Quaternion analytical mode decomposition (QAMD) is proposed to process multiple acoustic signals and extract fault information in industrial machinery systems with high sampling frequency, low speed, and heavy load. QAMD can separate characteristic information from frequency domain and extend it to the fault diagnosis of rotating industrial machinery. The multi-signal fusion method based on quaternion can process multiple sets of longer digital signals at the same time, which provides a new idea for the synchronous processing of big data. The proposed quaternion Fourier trend spectral segmentation method can not only automatically obtain bisecting frequencies and divide the signal into several frequency bands, but also realize the fusion and modal decomposition of multiple sets of digital signals in frequency domain. Experimental results show that the proposed method can effectively extract useful information from acoustic signals and apply it to bearing fault diagnosis.

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