Prediction of equipment maintenance using optimized support vector machine

Failure can be prevented in time by prediction of equipment maintenance so as to promote reliability only if failures can be early predicted. Substantially, it can be boiled down to a pattern recognition problem. Recenty, support vector machine (SVM) becomes a hot technique in this area. When using SVM, how to simultaneously obtain the optimal feature subset and SVM parameters is a crucial problem. This study proposes a method for improving SVM performance in two aspects at one time: feature subset selection and parameter optimization. Fuzzy adaptive particle swarm optimization (FAPSO) is used to optimize both a feature subset and parameters of SVM simultaneously for predictive maintenance. Case analysis shows that this algorithm is scientific and efficient, and adapts to predictive maintenance management for any complicated equipment.