Misfire identification in a four-stroke four-cylinder petrol engine using decision tree

Abstract

Misfire detection in an internal combustion engine is very crucial to maintain optimum performance throughout its service life and to reduce emissions. The vibration of the engine block contains indirect information regarding the condition of the engine. Misfire detection can be achieved by processing the vibration signals acquired from the engine using a piezoelectric accelerometer. This hidden information can be decoded using statistical parameters like kurtosis, standard deviation, mean, median, etc. This paper illustrates the use of decision tree as a tool for feature selection and feature classification. The effect of dimension, minimum number of objects and confidence factor on classification accuracy are studied and reported in this work.