Diagnosis and Prognosis of Bearing Failure in Rotating Machinery Using Acoustic Emission and Artificial Neural Network

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概要

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• 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.

• 2010-04-01

著者

• MAHAMAD Abd

  Department of Computer Science and Electrical Engineering of Kumamoto University

• HIYAMA Takashi

  Graduate School of Science and Technology, Kumamoto University

• Ghazali Mohd

  Faculty Of Mechanical And Manufacturing Engineering University Of Tun Hussein Onn Malaysia

• Hiyama Takashi

  Graduate School Of Science And Technology Kumamoto University

• Hiyama Takashi

  Department Of Computer Science And Electrical Eng. Kumamoto University

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