Predicting remaining useful life of rotating machinery based artificial neural network

Mahamad, Abd Kadir and Saon, Sharifah and Hiyama, Takashi Predicting remaining useful life of rotating machinery based artificial neural network. Computers and Mathematics with Applications, 60 . pp. 1078-1087. ISSN 0898-1221

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Abstract

Accurate remaining useful life (RUL) prediction of machines is important for condition based maintenance (CBM) to improve the reliability and cost of maintenance. This paper proposes artificial neural network (ANN) as a method to improve accurate RUL prediction of bearing failure. For this purpose, ANN model uses time and fitted measurements Weibull hazard rates of root mean square (RMS) and kurtosis from its present and previous points as input. Meanwhile, the normalized life percentage is selected as output. By doing that, the noise of a degradation signal from a target bearing can be minimized and the accuracy of prognosis system can be improved. The ANN RUL prediction uses FeedForward Neural Network (FFNN) with Levenberg Marquardt of training algorithm. The results from the proposed method shows that better performance is achieved in order to predict bearing failure.

Item Type:Article
Uncontrolled Keywords:RUL; ANN; bearing; prediction; FFNN
Subjects:Q Science > QA Mathematics > QA76 Computer software
Divisions:Faculty of Electrical and Electronic Engineering > Department of Computer Engineering
ID Code:3031
Deposited By:Normajihan Abd. Rahman
Deposited On:14 Feb 2013 15:19
Last Modified:22 Jan 2015 11:50

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