TELKOMNIKA Telecommunication, Computing, Electronics and Control
Spark plug failure detection using Z-freq and machine learning

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Spark plug failure detection using Z-freq and machine learning

Engine failure
Machine learning
Misfire analysis
Statistical analysis
Z-freq

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.

Nor Azazi Ngatiman, Mohd Zaki Nuawi, Azma Putra, Isa S. Qamber, Tole Sutikno, Mohd Hatta Jopri

http://journal.uad.ac.id/index.php/TELKOMNIKA

Nov 10, 2021

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