Short-term fault prediction based on support vector machines with parameter optimization by evolution strategy

Authors:
Shumin Hou;Yourong Li
Affiliations:
Hubei Province Key Lab. of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, P.O. Box 222, Hubei 430081, People's Republic of China and Departm ...;Hubei Province Key Lab. of Machine Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, P.O. Box 222, Hubei 430081, People's Republic of China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Support vector machines (SVMs) are the effective machine-learning methods based on the structural risk minimization (SRM) principle, which is an approach to minimize the upper bound risk functional related to the generalization performance. The parameter selection is an important factor that impacts the performance of SVMs. Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) is an evolutionary optimization strategy, which is used to optimize the parameters of SVMs in this paper. Compared with the traditional SVMs, the optimal SVMs using CMA-ES have more accuracy in predicting the Lorenz signal. The industry case illustrates that the proposed method is very successfully in forecasting the short-term fault of large machinery.