Diesel Engine Inlet and Exhaust System Fault Detection Based on PSO-BP NN

Abstract

There is high failure rate in diesel engine inlet and exhaust system, so the inlet and exhaust system condition monitoring and fault diagnosis is of great significance.To overcome drawbacks of pure BP algorithm, a heuristic algorithm is adopted to give a transition from particle swarm search to gradient descending search to set up a Particle Swarm Optimized Back Propagation (PSO-BP) Neutral Network (NN). Then vibration signals have been measured from a diesel cylinder head by simulating two faults:the gas leak and abnormal lash; The diesel engine vibration frequency energy signals after wavelet packet decomposition are taken as the input feature vectors of NN. Finally, we carry out fault detection experiment by the PSO-BP NN model to validate the method. Experiment results show that the PSO-BP NN has more fast convergence speed and higher diagnosis accuracy than BP NN, and it provides a new fault detection method for engine inlet and exhaust system.