A genetic algorithm to optimize the adaptive Support Vector Regression model for forecasting the reliability of diesel engine systems

This paper presents the use of the Support Vector Regression (SVR) technique to forecast the reliability of a system. Future predicted values of system reliability are highly sensitive to the choice of SVR parameters and the type of kernel SVR function. The dataset of a turbocharged diesel engine system was used as a case study. The Normalize Root Mean Square Error (NRMSE) measure was used to evaluate the SVR model in predicting the reliability of the system. Many experimental attempts were done using the optimal SVR parameters and the proper kernel function. Results showed that Order 5 of the polynomial kernel outperformed both Gaussian and linear kernel functions in predicting the future reliability values with minimal NRMSE. Experimentally, choosing the proper parameters for the SVR is a hard process, and there are no guarantees that the good parameters and the best kernel function are used. Therefore, artificial intelligence must be used. A genetic algorithm (GA) was used as an AI search optimization method to optimize both the SVR parameters and the type of the kernel function by generating a GA-SVR model. The GA successfully optimized the SVR model to ensure accurate predictions. The adaptive GASVR model was used to overcome such problems as small size of the dataset, varying lifetimes of the system components, and odd behavior of the system because of external environmental causes. Results confirmed the efficiency of the adaptive model to predict precisely the reliability of the system, even with a small dataset.

[1]  Peter J. B. Hancock.  A comparison of selection mechanisms , 1997 .

[2]  Shuqiang Yang,et al.  Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism , 2014, TheScientificWorldJournal.

[3]  Loon Ching Tang,et al.  Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..

[4]  Zhao Yan,et al.  Application of Support Vector Machine to Reliability Analysis of Engine Systems , 2013 .

[5]  Xiaoping Du,et al.  Improved Reliability-Based Optimization with Support Vector Machines and Its Application in Aircraft Wing Design , 2015 .

[6]  Bonthu Kotaiah,et al.  A Survey on Software Reliability Assessment by Using Different Machine Learning Techniques , 2012 .

[7]  Reyadh Naoum,et al.  Comparison of Selection Methods and Crossover Operations using Steady State Genetic Based Intrusion Detection System , 2012 .

[8]  K. M. George,et al.  A Reliability Measure for Time Series Forecasting Predictor , 2015 .

[9]  Gaurav Aggarwal,et al.  Neural Network Approach to Measure Reliability of Software Modules: A Review , 2013 .

[10]  Ryan Champlin,et al.  Selection Methods of Genetic Algorithms , 2018 .

[11]  Ping-Feng Pai,et al.  System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..

[12]  Probal Chaudhuri,et al.  On The Use of Genetic Algorithm with Elitism in Robust and Nonparametric Multivariate Analysis , 2003 .

[13]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[14]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[15]  Lin Ge,et al.  Bayesian Network Based Software Reliability Prediction by Dynamic Simulation , 2013, 2013 IEEE 7th International Conference on Software Security and Reliability.