Artificial Intelligence Based Green Technology Retrofit for Misfire Detection in Old Engines

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. DOI: 10.4018/jgc.2012010104 44 International Journal of Green Computing, 3(1), 43-55, January-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1993). Engine misfire is defined as, “lack of combustion in the cylinder due to absence of spark, poor fuel metering, poor compression, or any other cause” (California Air Resources Board, 1991). Misfire detection in an internal combustion engine is very crucial to maintain optimum performance throughout its service life and to reduce emissions.