Sparkplug failure detection using Z-freq and machine learning

Ngatiman, Nor Azazi and Nuawi, Mohd Zaki and Putra, Azma and Qamber, Isa S. and Tole, Sutikno and Jopri, Mohd Hatta (2021) Sparkplug failure detection using Z-freq and machine learning. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19 (6). pp. 2020-2029. ISSN 1693-6930

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Abstract

Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.

Item Type:	Article
Uncontrolled Keywords:	Engine failure, Machine learning, Misfire analysis, Statistical analysis, Z-freq
Divisions:	Faculty of Mechanical and Manufacturing Engineering Technology
Depositing User:	Sabariah Ismail
Date Deposited:	13 Apr 2022 15:53
Last Modified:	13 Apr 2022 15:53
URI:	http://eprints.utem.edu.my/id/eprint/25847
Statistic Details:	View Download Statistic

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