Reliability prediction using support vector regression

Reliability prediction of machinery is crucial to schedule overhauls, spare parts replacement and maintenance interventions. Many AI tools have been used in order to provide these predictions for the industry. Support vector regression (SVR) is a nonlinear regression technique extended from support vector machine. SVR can fit data flexibly and it has a wide variety of applications. This paper utilizes SVR combining time series to predict the next failure time based on historical failure data. To solve the parameter selection problem a method has been proposed. This method approximates the widely used leave-one-out method. To bound the prediction error, a confidence interval is proposed based on the non-homogeneous poisson process. A numerical case from the mining industry is presented to demonstrate the developed approach.

[1]  Uday Kumar.  Development of maintenance policies for mines : a statistical approach , 1990 .

[2]  D. Das,et al.  A review of reliability prediction methods for electronic devices , 2002, Microelectron. Reliab..

[3]  S. González,et al.  Analysis of Video-Movies Using Support Vector Regression , 2010 .

[4]  Mohammad Modarres,et al.  Generalized renewal process for analysis of repairable systems with limited failure experience , 2002, Reliab. Eng. Syst. Saf..

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Chih-Chieh Yang,et al.  A support vector regression based prediction model of affective responses for product form design , 2010, Comput. Ind. Eng..

[7]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[8]  Y. Radhika,et al.  Atmospheric Temperature Prediction using Support Vector Machines , 2009 .

[9]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[10]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[11]  Gavin C. Cawley,et al.  Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers , 2003, Pattern Recognit..

[12]  M. Lefebvre.  Applied probability and statistics , 2006 .

[13]  Marvin Zelen,et al.  Mathematical Theory of Reliability , 1965 .

[14]  Dorit S. Hochbaum,et al.  Approximation Algorithms for NP-Hard Problems , 1996 .

[15]  Thomas G. Dietterich.  Adaptive computation and machine learning , 1998 .

[16]  Marvin Rausand,et al.  System Reliability Theory: Models, Statistical Methods, and Applications , 2003 .

[17]  Weifeng Liu,et al.  Adaptive and Learning Systems for Signal Processing, Communication, and Control , 2010 .

[18]  Ping Guo,et al.  Support Vector Regression for Software Reliability Growth Modeling and Prediction , 2005, ISNN.

[19]  Uday Kumar,et al.  Goodness-of-fit tests for the power-law process based on the TTT-plot , 1992 .

[20]  Kuan-Yu Chen,et al.  Forecasting systems reliability based on support vector regression with genetic algorithms , 2007, Reliab. Eng. Syst. Saf..

[21]  Marina L. Gavrilova,et al.  Rotation Invariant Multiview Face Detection Using Skin Color Regressive Model and Support Vector Regression , 2010, Int. J. Pattern Recognit. Artif. Intell..

[22]  Uday Kumar,et al.  Complex System Reliability Evaluation using Support Vector Machine for Incomplete Data-set , 2010 .