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Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy

by Songrong Luo 1,2,*, Wenxian Yang 3 and Youxin Luo 1,2
1
Hunan Provincial Cooperative Innovation Center for the Construction & Development of Dongting Lake Ecological Economic Zone, Changde 415000, China
2
College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415000, China
3
School of Engineering, Newcastle University NE1 7RU, UK
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(4), 375; https://doi.org/10.3390/e22040375
Received: 10 March 2020 / Revised: 23 March 2020 / Accepted: 23 March 2020 / Published: 25 March 2020
Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonlinear measures to reveal the hidden fault-sensitive information. Thirdly, a basic Multi-Class Support Vector Machine (multiSVM) serves as a classifier to automatically identify the fault type and fault location. Finally, experimental analysis and comparison are made to verify the effectiveness and superiority of the proposed model. The results show that the RCMDE value lead to a larger difference between various states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing. View Full-Text
Keywords: adaptive sparest narrow-band decomposition; multiscale analysis; refined composite multiscale dispersion entropy; fault diagnosis adaptive sparest narrow-band decomposition; multiscale analysis; refined composite multiscale dispersion entropy; fault diagnosis
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Luo, S.; Yang, W.; Luo, Y. Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy. Entropy 2020, 22, 375.

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