MISFIRE DETECTION IN I.C. ENGINE USING MACHINE LEARNING APPROACH

Abstract

Misfire is one of the major problem associated with the engine as it leads to power loss along with exhaust of air-pollutants like HC, CO, and NOx. Maintenance and condition monitoring of an IC engine is a very crucial activity which requires restriction of emission to the least possible levels. For misfire detection, vibration signals from engine cylinder were obtained using the piezoelectric accelerometer. As engine misfire gives specific vibration signal pattern with respect to the cylinder where misfire took place. Further, 12 statistical features like Standard Error, Sample Variance, Skewness etc. were extracted from obtained signals. Out of these, only useful features were identified using the J48 decision tree algorithm. Classification via Regression, IBk were used as classifiers for classification of these selected features. This paper deals with the comparative study of these classifiers and ensembling these classifiers using Vote classifier and from that, the better algorithm for misfire detection system is suggested.