Universiti Teknologi Malaysia Institutional Repository

A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis

Hui, K. H. and Ooi, C. S. and Lim, M. H. and Leong, M. S. (2016) A hybrid artificial neural network with dempster-shafer theory for automated bearing fault diagnosis. Journal of Vibroengineering, 18 (7). pp. 4409-4418. ISSN 1392-8716

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Bearing fault diagnosis has a pivotal role in condition-based maintenance. Vibration spectra analysis has been proven to be the most efficient method for rotating machinery fault diagnosis. Vibration spectra can be analyzed by various signal processing tools (e.g. wavelet analysis, empirical mode decomposition, Hilbert-Huang transform). However, they involve human expertise in ensuring its maximum success. Machine learning tools (e.g. artificial neural networks (ANN), support vector machines (SVM)) can be an alternative for an automatic fault diagnosis. Researchers have studied the feasibility of ANN for automatic fault diagnosis since last decades. Most of the researchers reported positive finding in adapting ANN for automatic fault diagnosis. However, its accuracy is highly dependent on the neural networks structure such as number of nodes, hidden layers, and sigmoid function. This study proposed a hybrid algorithm used for automated bearing fault diagnosis based on ANN and Dempster-Shafer (DS) theory. The hybrid algorithm employed DS theory to improve the fault diagnosis results from ANN by eliminating conflicting results generated by ANN. Four conditions of bearing namely healthy condition and three types of faults included ball, inner race, and outer race faults classify by the proposed hybrid algorithm and artificial neural networks. The superiority of the hybrid algorithm was shown by comparing its result with the performance of ANN alone.

Item Type:Article
Uncontrolled Keywords:Electric fault currents, Failure analysis, Formal logic, Learning systems, Machinery, Mathematical transformations, Neural networks, Signal processing, Support vector machines, Vibration analysis, Wavelet analysis, Wavelet decomposition, Automatic fault diagnosis, Bearing fault, Condition based maintenance, Dempster-shafer, Empirical Mode Decomposition, Hybrid artificial neural network, Machinery fault diagnosis, Neural networks structure, Fault detection
Subjects:T Technology > T Technology (General)
Divisions:Malaysia-Japan International Institute of Technology
ID Code:71665
Deposited By: Widya Wahid
Deposited On:21 Nov 2017 03:28
Last Modified:21 Nov 2017 03:28

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