Prediction of Vehicle Reliability using ANN
Volume 8, Number 3, May 2012 - Paper 9 - pp. 321-329
B. HARI PRASAD1, P. BHATTACHARJEE2 and A. VENUGOPAL31 Military College of EME, Secunderabad-500 015, India
2 Defence Research and Development Laboratory, Hyderabad - 500 058, India
3 National Institute Technology, Warangal - 506 004, India
(Received on March 23, 2010, revised on August 13, 2011 and February 18, 2012)
ANNs are usually very effective as computational tools and have found extensive utilisation in solving many complex real-world problems. The attractiveness of ANNs comes from their remarkable information processing characteristics pertinent mainly to nonlinearity, high parallelism, fault and noise tolerance besides its learning and generalisation capabilities. The aim of this paper is to familiarise with ANN-based computing (neuro-computing). The predicted and observed vehicle reliability using trained ANN is very close as compared to Weibull probability distribution. The methodology adopted is demonstrated with the help of a case study which includes collection, sorting and grouping of vehicle failure data. Then distribution parameters are estimated and best fitting probability distribution is identified for predicting vehicle reliability. Subsequently the trained ANN (using SLP model) is used to predict the vehicle reliability. Suitability of a RDBMS (Oracle) for training ANN and predicting vehicle reliability is also presented. The developed methodology has been able to predict reliability of vehicle very close to its observed values.
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