Misfire Detection of Automotive Engines with Convolutional Neural Network

Abstract

Misfiring in multi-cylinder is caused due to abnormal combustion of the engine. Cylinders produce less power if engine misfires. Engine misfires generate unburned hydrocarbons and increase the load on the catalytic converter. The job of the catalytic converter is to convert harmful pollutants into less harmful emissions before they ever leave the vehicle exhaust system. If the misfire rate is excessive, the catalytic converter can overheat and be damaged. Hence engine misfire becomes a severe issue and should be detected in early stages. During misfire and at normal combustion, every cylinder has its own unique vibration spectrum this will be a very useful phenomenon for misfire detection. In this work, vibration signals are recorded using a piezoelectric accelerometer for normal combustion as well as combustion with a misfire for each cylinder. The obtained vibration signals are used for training and testing of conventional machine learning algorithm as well as the deep learning algorithm. Support Vector Machine (SVM) with statistical feature extraction as conventional machine learning algorithm and Convolutional Neural Network (CNN) as deep learning algorithm are used for misfire detection in multi-cylinder. In this paper, the evaluation parameters of classifiers such as overall accuracy, precision, recall etc. are compared. Deep learning algorithm i.e. CNN without feature Extraction was found to be performing better for multiclass misfire detection compared to CNN with feature extraction and SVM.