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2022 | OriginalPaper | Chapter

Misfire Prediction on Spark Ignition Four-Stroke Engine Through Statistical Features Using Rough Set Theory Classifier

Authors : Joshuva Arockia Dhanraj, Jenoris Muthiya Solomon, Mohankumar Subramaniam, Meenakshi Prabhakar, Christu Paul Ramaian, Nandakumar Selvaraju, Nadanakumar Vinayagam

Published in: Technology Innovation in Mechanical Engineering

Publisher: Springer Nature Singapore

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Abstract

Misfire is one of the key challenges engines encounter because it adds to the power loss amid air pollutants such as CO and NOx from the exhaust gas. Due to a certain cylinder, discrepancy produces a special pattern of vibration. These patterns can extract useful properties and analyze them to detect misfire. This paper aims to use a machine learning method a misfire identification comprehensive framework. Vibration signals have been used in the present analysis (via piezoelectric accelerometer) as a form of misfire that is unique to each cylinders. Statistical features were then derived and used the J48 from the obtained features, and the feature selection is implemented. The roughest theory classifier was used in the classification of the misfire in the cylinder. In Maruti Suzuki Baleno, the experiment was tested and all cylinder misfire testing was carried out for individual cylinders separately. Through tenfold cross-validation in WEKA, the classifier output was validated.

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Metadata
Title
Misfire Prediction on Spark Ignition Four-Stroke Engine Through Statistical Features Using Rough Set Theory Classifier
Authors
Joshuva Arockia Dhanraj
Jenoris Muthiya Solomon
Mohankumar Subramaniam
Meenakshi Prabhakar
Christu Paul Ramaian
Nandakumar Selvaraju
Nadanakumar Vinayagam
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Technology Innovation in Mechanical Engineering

Print ISBN: 978-981-16-7908-7

Electronic ISBN: 978-981-16-7909-4

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-7909-4_12