Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (9): 1488-1496.doi: 10.23940/ijpe.20.09.p18.14881496

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Misfire Fault Diagnosis of Automobile Engine based on Time Domain Vibration Signal and Probabilistic Neural Network

Canyi Du, Wen Li, Feifei Yu, Feng Li*, and Xiangkun Zeng   

  1. School of Automotive and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:

Abstract: Engine misfire fault diagnosis model based on probabilistic neural network (PNN) is established, in which the vibration acceleration signal of engine cylinder block surface is used as diagnosis parameter. The time domain signal of vibration acceleration is directly used as network input by virtue of the advantage of PNN in rapid processing of large and complex data. Simplify the diagnostic process. In the PNN, the dispersion constant "spread" value determines the sensitivity and stability of the network model. Aiming at this problem, the particle swarm intelligent algorithm, which can achieve efficient parallel search, is used to find the optimal "spread" value to ensure the best diagnosis effect. The experimental results show that the accuracy can reach 100%, and the fault tolerance rate is high in the non-ideal state, which shows that this method is feasible and convenient.

Key words: vibration signal, PNN neural network, misfire fault, diagnose