Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing

Abstract

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.

RETRACTED: Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

“…At this time, the DE algorithm is extremely sensitive to noise. Therefore, an integer between c ¼ [4,8] is selected. The case of c ¼ 6 is selected in this paper.…”

“…Bearing experiment achievement show that approximate entropy has a good performance in measuring the complexity of signal. 4 Zheng's research group used the combination of sample entropy and multiscale analysis for fault diagnosis of rotating machinery, and achieved good results. 5,6 However, the approximate entropy has the defect of self-matching, and it is susceptible to noise.…”

Compound Fault Diagnosis for Rotating Machinery: State-of-the-Art, Challenges, and Opportunities

“…Signal decomposition-based methods are similar to pattern recognition that relied on feature engineering, in which the different components are expected to be separated. Scholars and researchers have proposed lots of successful methods for compound fault diagnosis, such as Wavelet Transform (WT) [20][21][22][23][24][25][26][27][28], Variational Mode Decomposition (VMD) [29][30][31][32][33][34], Local Mean Decomposition (LMD) [35], Singular Spectrum Decomposition (SSD) [36,37], Symplectic Geometry Mode Decomposition (SGMD) [38,39], and other methods [40][41][42][43][44][45][46][47][48]. first, the compound fault signals are separated into different empirical models by empirical WT; second, a duffing oscillator which incorporates all single fault frequency is used to establish the fault isolator; finally, all the single faults can be recognized one by one by observing the chaotic motion from the Poincar mapping of the fault isolator outputs [20].…”

“…For instance, Tang et al proposed a compound fault detection method with virtual multichannel signals in the angel domain and applied it to monitoring the rolling bearings under varying working conditions [43]. More details can be found in [40][41][42][43][44][45][46][47][48], which are not enumerated here. and Cyclostationary Blind Deconvolution (CYCBD) [63], can enhance weak periodic features and suppress signal noise by constructing a comb filter, thus, have been proven to be an effective tool for separating compound fault with weak components.…”

Enhancement of Fault Feature Extraction from Displacement Signals by Suppressing Severe End Distortions via Sinusoidal Wave Reduction

“…On the other hand, due to complex structures of rotating machinery, the acquired vibration signals often appear as mixtures of multiple vibration modes [7,8]. As it is difficult to identify incipient non-stationary features using methods based on pure time domain or pure frequency domain, signal decompositions are usually required decompose the original measurement [9,10].…”