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

Lookup NU author(s): Dr Wenxian Yang

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

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 topresent 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 betweenvarious states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing.


Publication metadata

Author(s): Luo S, Yang W, Luo Y

Publication type: Article

Publication status: Published

Journal: Entropy

Year: 2020

Volume: 22

Issue: 4

Online publication date: 25/03/2020

Acceptance date: 23/03/2020

Date deposited: 25/03/2020

ISSN (electronic): 1099-4300

Publisher: MDPI AG

URL: https://doi.org/10.3390/e22040375

DOI: 10.3390/e22040375


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