Open Access

ARTICLE

Vibration-Based Fault Diagnosis Study on a Hydraulic Brake System Using Fuzzy Logic with Histogram Features

Alamelu Manghai T Marimuthu¹, Jegadeeshwaran Rakkiyannan^2,*, Lakshmipathi Jakkamputi¹, Sugumaran Vaithiyanathan¹, Sakthivel Gnanasekaran²

1 School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India
2 Centre for Automation, School of Mechanical Engineering, Vellore Institute of Technology, Chennai, 600127, India

* Corresponding Author: Jegadeeshwaran Rakkiyannan. Email:

Structural Durability & Health Monitoring 2022, 16(4), 383-396. https://doi.org/10.32604/sdhm.2022.011396

Received 06 May 2020; Accepted 06 December 2021; Issue published 03 January 2023

Abstract

The requirement of fault diagnosis in the field of automobiles is growing higher day by day. The reliability of human resources for the fault diagnosis is uncertain. Brakes are one of the major critical components in automobiles that require closer and active observation. This research work demonstrates a fault diagnosis technique for monitoring the hydraulic brake system using vibration analysis. Vibration signals of a rotating element contain dynamic information about its health condition. Hence, the vibration signals were used for the brake fault diagnosis study. The study was carried out on a brake fault diagnosis experimental setup. The vibration signals under different fault conditions were acquired from the setup using an accelerometer. The condition monitoring of the hydraulic brake system using the vibration signal was processed using a machine learning approach. The machine learning approach has three phases, namely, feature extraction, feature selection, and feature classification. Histogram features were extracted from the vibration signals. The prominent features were selected using the decision tree. The selected features were classified using a fuzzy classifier. The histogram features and the fuzzy classifier combination produced maximum classification accuracy than that of the statistical features.

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Keywords

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