A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine

UM > Faculty of Science and Technology

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	A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine
	Wong, P. K.; Zhong, J.H.; Yang, Z. X.; Vong, C. M.
	2017-03-01
Source Publication	Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (SCI-E)
ISSN	2041-2983
Pages	1146-1161
Abstract	This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneousfault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as different signal committee members, and each member is trained using vibration or sound signals respectively. The individual diagnostic result from each fault detection member is then combined by a new probabilistic ensemble method, which can improve the overall diagnostic accuracy and increase the number of detectable fault as compared to individual classifier acting alone. The effectiveness of the proposed framework is verified by a case study on a gearbox fault detection. Experimental results show the proposed framework is superior to the existing single probabilistic classifier. Moreover, the proposed system can diagnose both single- and simultaneous-faults for the rotating machinery while th
Keyword	Rotating machinery simultaneous-fault diagnosis pairwise-coupled sparse Bayesian extreme learning machine probabilistic committee machine
Language	英語English
The Source to Article	PB_Publication
PUB ID	20352
Document Type	Journal article
Collection	Faculty of Science and Technology
Corresponding Author	Yang, Z. X.
Recommended Citation GB/T 7714	Wong, P. K.,Zhong, J.H.,Yang, Z. X.,et al. A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine[J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (SCI-E), 2017, 1146-1161.
APA	Wong, P. K.., Zhong, J.H.., Yang, Z. X.., & Vong, C. M. (2017). A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (SCI-E), 1146-1161.
MLA	Wong, P. K.,et al."A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine".Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science (SCI-E) (2017):1146-1161.

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