Due to the poor working conditions of an engine, its control system is prone to failure.\nIf these faults cannot be treated in time, it will cause great loss of life and property. In order to\nimprove the safety and reliability of an aero-engine, fault diagnosis, and optimization method of\nengine control system based on probabilistic neural network (PNN) and support vector machine\n(SVM) is proposed. Firstly, using the German 3Wpiston engine as a control object, the fault diagnosis\nscheme is designed and introduced briefly. Then, the fault injection is performed to produce faults,\nand the data sample for engine fault diagnosis is established. Finally, the important parameters of\nPNN and SVM are optimized by particle swarm optimization (PSO), and the results are analyzed and\ncompared. It shows that the engine fault diagnosis method based on PNN and SVM can effectively\ndiagnose the common faults. Under the optimization of PSO, the accuracy of PNN and SVM results\nare significantly improved, the classification accuracy of PNN is up to 96.4%, and the accuracy of SVM\nis up to 98.8%, which improves the application of them in fault diagnosis technology of aero-piston\nengine control system.
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