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,Jegadeeshwaran R

doi:10.36001/ijphm.2019.v10i1.2748

Abstract

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 Repeated Incremental Pruning to Produce Error Reduction ( 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.

Highlights

A brake is an important controlling element which consists of a combination of interacting parts that work to slow down a vehicle
Histogram feature extraction techniques were used for extracting the vibration signal under each condition
The selected features were classified using the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and fuzzy unordered rule induction algorithm (FURIA) algorithm as discussed below feature selection and feature classification process have been explained in this study

Summary

Introduction

A brake is an important controlling element which consists of a combination of interacting parts that work to slow down a vehicle. Any failure in the brake system makes an impact on vehicle stability and the passenger's safety. Preventive maintenance of the hydraulic brake system is crucial in order to avoid damage. This is achieved through the condition monitoring process. Condition monitoring is defined as the continuous evaluation of the health of the part and equipment throughout its service life.

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Results

Conclusion