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

The Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China
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Entropy 2019, 21(5), 490; https://doi.org/10.3390/e21050490
Received: 24 April 2019 / Revised: 6 May 2019 / Accepted: 11 May 2019 / Published: 13 May 2019
(This article belongs to the Special Issue Entropy-Based Fault Diagnosis)
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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.
Keywords: rolling bearing; compound fault; negentropy spectrum decomposition; fast empirical wavelet transform; spectral negentropy rolling bearing; compound fault; negentropy spectrum decomposition; fast empirical wavelet transform; spectral negentropy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xu, Y.; Chen, J.; Ma, C.; Zhang , K.; Cao, J. Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing. Entropy 2019, 21, 490.

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