Predicting remaining useful life of rotating machinery based artificial neural network

Authors:
Abd Kadir Mahamad;Sharifah Saon;Takashi Hiyama
Affiliations:
Department of Computer Science and Electrical Engineering, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan and Faculty of Electrical and Electronic Engineering, Universiti Tun Husse ...;Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia;Department of Computer Science and Electrical Engineering, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan
Venue:
Computers & Mathematics with Applications
Year:
2010

Citing 5
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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.