Sparkplug failure detection using Z-freq and machine learning

• Main Authors: Ngatiman, Nor Azazi, Nuawi, Mohd Zaki, Putra, Azma, Qamber, Isa S., Tole, Sutikno, Jopri, Mohd Hatta
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2021
• Online Access: http://eprints.utem.edu.my/id/eprint/25847/2/22027-59268-1-PB.PDF; http://eprints.utem.edu.my/id/eprint/25847/; http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/22027/10963

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.