Open AccessRolling bearing fault diagnosis method based on OSFFDM and adaptive multi-scale weighted morphological filtering
Author(s) -
Siqi Huang,
Xinglong Wang,
Siguo Yang,
Zhiyin Tan
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2246/1/012044
Subject(s) - algorithm , kurtosis , bearing (navigation) , computer science , smoothing , noise (video) , envelope (radar) , filter (signal processing) , interference (communication) , signal (programming language) , fault (geology) , mathematics , artificial intelligence , statistics , channel (broadcasting) , computer vision , telecommunications , radar , image (mathematics) , programming language , seismology , geology
The Order-statistic filtering Fourier decomposition (OSFFDM) is a decomposition method that obtains components of different frequency bands by pre-processing the Fourier spectrum. The OSFFDM method overcomes the problem of a large number of invalid components in FDM. However, OSFFDM only considers the frequency band search problem, and does not really solve the interference problem of noise and irrelevant components. To solve this problem, a bearing fault diagnosis method named OSFFDM and adaptive multi-scale weighted morphological filtering (AMWMF) is proposed. First, the order-statistic filtering and smoothing methods are used to fit the envelope trend term of the Fourier frequency spectrum of the raw signal. Second, according to the envelope trend, a series of single components are obtained through the idea of segmentation and reconstruction. Then, the AMWMF is used to filter the component with the maximum kurtosis value. Finally, the envelope spectrum is used to analyze the filtered signal. In the analysis of the actual collected bearing vibration signal, the diagnostic results of the combination of OSFFDM and AMWMF and existing methods such as EMD and FDM are studied and compared. From the comparison results, it can be observed that the OSFFDM and AMWMF method can effectively identify bearing fault information. By calculating the signal-to-noise ratio (SNR) of the optimal component, the proposed method has a higher SNR, that is, less noise interference. The comparison of the diagnosis results further verifies the effectiveness and superiority of the OSFFDM and AMWMF method.