Crack detection in rotating shafts using wavelet analysis, Shannon entropy and multi-class SVM

Huo, Zhiqiang and Zhang, Yu and Zhou, Zhangbing and Huang, Jianfeng (2017) Crack detection in rotating shafts using wavelet analysis, Shannon entropy and multi-class SVM. In: 3rd EAI International Conference on Industrial Networks and Intelligent Systems, 4 September 2017, Ho Chi Minh City, Vietnam.

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Abstract

Incipient fault diagnosis is essential to detect potential abnormalities and failures in industrial processes which contributes to the implementation of fault-tolerant operations for minimizing performance degradation. In this paper, an innovative method named Self-adaptive Entropy Wavelet (SEW) is proposed to detect incipient transverse crack faults on rotating shafts. Continuous Wavelet Transform (CWT) is applied to obtain optimized wavelet function using impulse modelling and decompose a signal into multi-scale wavelet coefficients. Dominant features are then extracted from those vectors using Shannon entropy, which can be used to discriminate fault information in different conditions of shafts. Support Vector Machine (SVM) is carried out to classify fault categories which identifies the severity of crack faults. After that, the effectiveness of this proposed approach is investigated in testing phrase by checking the consistency between testing samples with obtained model, the result of which has proved that this proposed approach can be effectively adopted for fault diagnosis of the occurrence of incipient crack failures on shafts in rotating machinery.

Keywords:Fault diagnosis, Shaft, Continuous wavelet transform, Shannon entropy, Multi-class SVM
Subjects:H Engineering > H300 Mechanical Engineering
Divisions:College of Science > School of Engineering
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ID Code:27763
Deposited On:29 Jun 2017 09:18

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