Diagnosis and prognosis of bearing failure in rotating machinery using acoustic emission and artificial neural network

Mahamad, Abd Kadir and Hiyama, Takashi and Ghazali, Mohd Imran Diagnosis and prognosis of bearing failure in rotating machinery using acoustic emission and artificial neural network. IEEJ Transactions on Industry Applications, 130 . pp. 443-449.

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Abstract

Bearing failure is well-known as a common problem in industries. Therefore, timely diagnosis and prognosis (DAP) of bearing fault is very crucial in order to prevent sudden damages. This paper proposes the practical method of bearing fault DAP using acoustic emission (AE) technique assisted with artificial neural network (ANN). The bearings failure data is measured based on the AE in terms of decibel (dB) and Distress levels, which are used as input for ANN of a bearing fault DAP. For this purpose, an experimental rig is setup to collect data from target bearing by using Machine Health Checker (MHC) Memo assisted with MHC Analysis software. In this work, Elman network with training algorithm, Levenberg-Marquardt Back- propagation is used for ANN DAP. The obtained results indicates that the proposed methods are suitable to inform the remaining useful life time of a faulty bearing.

Item Type:Article
Uncontrolled Keywords:diagnosis; prognosis; ANN; AE; bearing
Subjects:T Technology > TJ Mechanical engineering and machinery > TJ1040-1119 Machinery exclusive of prime movers
Divisions:Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
ID Code:3027
Deposited By:Normajihan Abd. Rahman
Deposited On:14 Feb 2013 14:38
Last Modified:22 Jan 2015 11:50

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