Application of FURIA for Finding the Faults in a Hydraulic Brake System Using a Vibration Analysis through a Machine Learning Approach

Alamelu Manghai T.M and Jegadeeshwaran R
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ijphm_19_016.pdf965.75 KBAugust 16, 2019 - 9:36pm

Vibration based continuous monitoring system for fault diagnosis of automobile hydraulic brake system is presented in this study. This study uses a machine learning approach for the fault diagnosis study. A hydraulic brake system test rig was fabricated. The vibration signals were acquired from the brake system under different simulated fault conditions using a piezoelectric transducer. The histogram features were extracted from the acquired vibration signals. The feature selection process was carried out using a decision tree. The selected features were classified using fuzzy unordered rule induction algorithm (FURIA) and RIPPER algorithm. The classification results of both algorithms for fault diagnosis of a hydraulic brake system were presented. Compared to RIPPER and J48 decision tree, the FURIA performs better and produced 98.73 % as the classification accuracy.

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Submission Keywords: 
Histogram Features
Confusion matrix.
J48 decision tree algorithm
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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