Universiti Teknologi Malaysia Institutional Repository

Dempster-shafer evidence theory for multi-bearing faults diagnosis

Kar, Hoou Hui and Meng, Hee Lim and Ali Al-Obaidi, Salah Mahdi and Leong, Mohd. Salman @ Yew Mun (2017) Dempster-shafer evidence theory for multi-bearing faults diagnosis. Engineering Applications of Artificial Intelligence, 57 . pp. 160-170. ISSN 0952-1976

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Official URL: http://dx.doi.org/10.1016/j.engappai.2016.10.017


Support vector machines (SVMs) are frequently used in automated machinery faults diagnosis to classify multiple machinery faults by handling a high number of input features with low sampling data sets. SVMs are well known for fault detection that involves binary fault classifications only (i.e., healthy vs. faulty). However, when SVMs are used for multi-faults diagnostics and classification, they result in a drop in classification accuracy; this is because the adaptation of SVMs for multi-faults classifications requires the reduction of the multiple classification problem into multiple subsets of binary classification problems that result in many contradictory results from each individual SVM model. To overcome this problem, a novel SVM-DS (Dempster-Shafer evidence theory) model is proposed to resolve conflicting results generated from each SVM model and thus increase the classification accuracy. The analysis of results shows that the proposed SVM-DS model increased the accuracy of the fault diagnosis model from 76% to 94%, as SVM-DS continuously refines and eliminates all conflicting results from the original SVM model. The proposed SVM-DS model is found to be more accurate and effective in handling multi-faults diagnostic and classification problems commonly faced in the industries, as compared to the original SVM method.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2016/05438
Uncontrolled Keywords:vibration, bearing fault diagnosis
Subjects:T Technology > TJ Mechanical engineering and machinery
T Technology
Divisions:Mechanical Engineering
Razak School of Engineering and Advanced Technology
ID Code:66481
Deposited By: Fazli Masari
Deposited On:03 Oct 2017 13:14
Last Modified:03 Oct 2017 13:14

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