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Misfire Detection in Spark-Ignition Engine using Statistical Learning Theory

Volume 12, Number 1, January 2016 - Paper 7 - pp. 79-88

ANISH BAHRI1, V.SUGUMARAN1, R. JEGADEESHWARAN1, and S.BABU DEVASENAPATI2

1 SMBS, VIT University, Chennai Campus, Chennai, India - 600127.
2 Principal, Sri Guru Institute of Technology, Coimbatore, India - 641110.

(Received on August 31, 2015, revised on October 5, 2015)

Abstract:

Misfire in an Internal Combustion engine is a serious problem that needs to be addressed to prevent engine power loss, fuel wastage and emissions. The vibration signal contains the vibration signature due to misfire and a combination of all vibration emissions of various engine components. The vibration signals acquired from the engine block are used here. Descriptive statistical features are used to represent the useful information stored in vibration signals. Out of all the statistical features, useful features were identified using the J48 decision tree algorithm and then the selected features were classified using logistic and simple logistic functions.  In this paper, performance analysis of logistic and simple logistic function has presented for detecting misfire in Spark Ignition (SI) Engine.

 

References: 20

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