A comprehensive approach for detecting brake pad defects using histogram and wavelet features with nested dichotomy family classifiers

Sakthivel Gnanasekaran, Lakshmi Pathi Jakkamputi, Jegadeeshwaran Rakkiyannan, Mohanraj Thangamuthu, Yogesh Bhalerao

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Abstract

The brake system requires careful attention for continuous monitoring as a vital module. This study specifically focuses on monitoring the hydraulic brake system using vibration signals through experimentation. Vibration signals from the brake pad assembly of commercial vehicles were captured under both good and defective conditions. Relevant histograms and wavelet features were extracted from these signals. The selected features were then categorized using Nested dichotomy family classifiers. The accuracy of all the algorithms during categorization was evaluated. Among the algorithms tested, the class-balanced nested dichotomy algorithm with a wavelet filter achieved a maximum accuracy of 99.45%. This indicates a highly effective method for accurately categorizing the brake system based on vibration signals. By implementing such a monitoring system, the reliability of the hydraulic brake system can be ensured, which is crucial for the safe and efficient operation of commercial vehicles in the market.
Original languageEnglish
Article number9093
JournalSensors
Volume23
Issue number22
Early online date10 Nov 2023
DOIs
Publication statusPublished - Nov 2023

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