Pattern Recognition based on Probabilistic Neural Network for Motorcycle Oil Fault Diagnosis

In the presented era, diagnosis of different types of faults that arise in vehicle engines especially in automotive of motorcycle is very critical in subject of repair and maintenance. Therefore, timely and accurate diagnosis of motorcycle lubricant oil fault is the key to solve the problem. In this...

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Bibliographic Details
Published in2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC) pp. 45 - 50
Main Authors Mutho' Affifah, Faisa Lailiyul, Darojah, Zaqiatud, Ningrum, Endah Suryawati
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
EMD
fault diagnosis
Lubricants
motorcycle
Motorcycles
Oils
Pattern recognition
PNN
Training
vibration
Vibrations
Viscosity
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Summary:In the presented era, diagnosis of different types of faults that arise in vehicle engines especially in automotive of motorcycle is very critical in subject of repair and maintenance. Therefore, timely and accurate diagnosis of motorcycle lubricant oil fault is the key to solve the problem. In this paper, motorcycle oil fault diagnosis is proposed through Empirical Mode Decomposition (EMD) as feature extraction and Probabilistic Neural Network (PNN) as pattern recognition. EMD such a Fourier transform and wavelet decomposition is used to break the signal than the selected feature is used as input into the PNN. PNN's pattern recognition is a classification method of a self-supervised and feedforward networks. Proposed scheme is evaluated on Honda Beat motorcycle which use AHM MPX2 lubricant oil based on LabView. Result shows that EMD decomposed vibration signal into its Intrinsic Mode Function (IMF) successfully, then become the input into PNN. Result has proven that PNN is quick learner, robust and has 100% classifying accuracy.
DOI:10.1109/KCIC.2018.8628596