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Misfire detection in I.C. engine through ARMA features using machine learning approach

Misfire detection in I.C. engine through ARMA features using machine learning approach One of the prime problems engines are facing is Misfire, as it leads to the power loss along with the exhaust gas containing air pollutants like CO and NOx. This paper proposes a predictive model for misfire detection using machine learning approach. For the present study, vibration signals acquired using the piezoelectric accelerometer were taken into consideration as a pattern of a misfire for each cylinder is specific in nature. Then, ARMA features were extracted from acquired vibration signals followed by Feature selection using J48 decision tree algorithm. For feature classification, the functional tree classifier was used. In this study, the classification accuracy of 92.2% was achieved. The proposed model was tested on the engine test rig wherein every cylinder misfire tests were conducted. This work can be improved by using different classifier algorithms for more accurate misfire detection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Progress in Industrial Ecology, an International Journal Inderscience Publishers

Misfire detection in I.C. engine through ARMA features using machine learning approach

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1476-8917
eISSN
1478-8764
DOI
10.1504/PIE.2018.095880
Publisher site
See Article on Publisher Site

Abstract

One of the prime problems engines are facing is Misfire, as it leads to the power loss along with the exhaust gas containing air pollutants like CO and NOx. This paper proposes a predictive model for misfire detection using machine learning approach. For the present study, vibration signals acquired using the piezoelectric accelerometer were taken into consideration as a pattern of a misfire for each cylinder is specific in nature. Then, ARMA features were extracted from acquired vibration signals followed by Feature selection using J48 decision tree algorithm. For feature classification, the functional tree classifier was used. In this study, the classification accuracy of 92.2% was achieved. The proposed model was tested on the engine test rig wherein every cylinder misfire tests were conducted. This work can be improved by using different classifier algorithms for more accurate misfire detection.

Journal

Progress in Industrial Ecology, an International Journal – Inderscience Publishers

Published: Jan 1, 2018

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