Misfire Detection in a Spark Ignition Engine using Support Vector Machines

To maintain optimum performance throughout the service life of an engine and to exercise a tight control over emissions, misfire detection is a vital activity. The engine block vibration contains valuable hidden information regarding the operating condition of the engine. Misfire can be detected by processing the vibration signals acquired from the engine using an accelerometer. The hidden information in the acquired signal can be analysed using various features extracted from the signals. A comparative performance analysis on classification accuracy of SVM when using statistical and histogram features for misfire detection in a spark ignition engine is presented.