Study on engine misfire fault based on neural networks and instantaneous angular speed

WANG Weikun, JIANG Zhinong, ZHANG Jinjie

(Diagnosis & SelfRecovery Engineering Research Center, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract: Aiming at the typical phenomenon of the engine misfire fault in the actual operation, the research was started from the instantaneous crankshaft speed of the engine. The relationship between the engine misfire fault and instantaneous crankshaft speed was briefly described. A mathematical model was established and the feature parameters were determined. Then, the Matlab was used to simulate it and BP neural networks were used to detect and analyse the engine misfire fault. The results indicate that there is a certain relationship between the engine misfire fault and the instantaneous crankshaft speed of the engine. Using instantaneous crankshaft speed can diagnose the engine misfire fault. Using BP neural networks can also diagnose the engine misfire fault and it has the features of fast and efficiently.

Key words: engine; misfire fault; BP neural networks; fault diagnosis