Paper Info
Title
Predicting remaining useful life of rotating machinery based artificial neural network
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.
Year
DOI
Venue
2010
10.1016/j.camwa.2010.03.065
Computers & Mathematics with Applications
Keywords
Field
DocType
prediction,rul,accurate remaining useful life,ffnn,ann model,ann,artificial neural network,weibull hazard rate,ann rul prediction,normalized life percentage,levenberg marquardt,feedforward neural network,bearing,accurate rul prediction,target bearing,hazard rate,root mean square
Weibull distribution,Bearing (mechanical),Artificial intelligence,Artificial neural network,Kurtosis,Condition-based maintenance,Mathematical optimization,Feedforward neural network,Pattern recognition,Root mean square,Machine learning,Mathematics,Levenberg–Marquardt algorithm
Journal
Volume
Issue
ISSN
60
4
Computers and Mathematics with Applications
Citations 
PageRank 
References 
7
0.73
5
Authors
3
Name
Order
Citations
PageRank
Abd Kadir Mahamad172.42
Sharifah Saon272.09
Takashi Hiyama3112.42