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The increasing dependence of internal combustion engine in multitudes of application has mandated a detailed study on most of its subsystems. This paper focuses on predictive maintenance using machine learning based models. The transmission system of any power pace is often challenged due to sudden variation in applied load. Any fault in the transmission system could lead to the catastrophic failures hence need for this work. This paper deals with the identification of various fault conditions that happen in a transmission system using vibration signals acquired by an accelerometer. The acquired signals are processed to extract the statistical and spectral features. These features are used to build a machine learning model using decision tree or Random forest algorithm. The best combination of features and algorithm is evaluated and the results are presented.

The core theme of the paper is misfire detection using random forest algorithm and decision tree based machine learning models for emission minimization in gasoline passenger vehicles. The engine block vibration signals are used for misfire detection. The signal is a combination of all vibration emissions of various engine components and also contains the vibration signature due to misfire. The quantum of information available at a given instant is enormous and hence suitable techniques are adopted to reduce the computational load due to redundant information. The random forest algorithm based model and the decision tree model are found to have a consistent high classification accuracy of around 89.7% and 89.3% respectively. From the results obtained the authors conclude that the combination of statistical features and random forest algorithm is suitable for detection of misfire in spark ignition engines and hence contributing to emission minimization in vehicles.