Abstract
In this study, support vector machine (SVM) theory is introduced into the fault diagnosis of diesel engine, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the diagnosis of engine accuracy. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters, improve the prediction accuracy of the model. Then, PSO-SVM is applied to predict the fault diagnosis of diesel engine, and compared with the traditional BP neural network (BPNN) and RBFNN network. The result shows that the prediction accuracy of PSO-SVM is greatly improved compared with BPNN and RBFNN, thus PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of diesel engine.
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