Fault Detection and Diagnosis of Diesel Engine Lubrication System Performance Degradation Faults based on PSO-SVM

Abstract

Considering the randomness and instability of the oil pressure in the lubrication system, a new approach for fault detection and diagnosis of diesel engine lubrication system based on support vector machine optimized by particle swarm optimization (PSO-SVM) model and centroid location algorithm has been proposed. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters. It can improve the prediction accuracy of the model. The results show that the classify accuracy of PSO-SVM is improved compared with SVM in which parameters are set according to experience. Then, the support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, diagnose algorithm is achieved through analyzing the centroid movement of features. According to Performance degradation data, degenerate trajectory model is established based on centroid location. And normal faults and performance degradation faults of diesel engine lubrication system are diagnosed. Results show that classification accuracy of the proposed PSO-SVM model achieved is 95.06% and 97.04% in two verify samples, it can meet the needs of fault diagnosis; and two typical faults and performance degradation fault of diesel engine can be diagnosed based on the proposed diagnosis method through simulation model based on AMESim.