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Misfire represents a crucial problem for vehicles, adding to the energy depletion in the midst of air pollution such as CO and NOx caused by exhaust gases. Because of a special cylinder, misfire produces a particular vibration pattern. These patterns can isolate and interpret valuable properties to detect misfires. In this paper, a machine learning approach is used as a predictive model for the identification of misfires. In the current research, vibratory signals were taken as a kind of misfire that is unique to each cylinder (acquired with the help of a piezoelectric accelerometer). Statistical characteristics are then extracted and feature selection is applied using the J48 decision tree algorithm from the features obtained. In the classification of misfires in the cylinders, the K* classification was used. The experiment was conducted in Maruti Suzuki Baleno. Every single cylinder was tested on a separate basis.

Exhaust after treatment devices in diesel engines play a crucial role in control of harmful emissions. The noxious emission released from diesel engines causes a variety of problems to both human beings and the environment. The currently used devices are implemented with new catalyst technologies like DOC, SCR and catalytic converter are all designed to meet stringent emission regulations. Although these devices have considerable conversion efficiency, they are not without drawbacks. The catalysts used in these devices are rarely available and are also very expensive. Diesel Particulate Filter (DPF) is the device currently employed to collect particulate matter. It also has drawbacks like high back pressure, thermal durability restrictions, regeneration issues and poor collection of smaller size particles. In the case of biodiesel these fine sized particles are emitted in larger quantity.