Prediction of Brake Pad Wear Using Various Machine Learning Algorithms

Steffan, John Jeyaraj; Jebadurai, Immanuel Johnraja; Asirvatham, Lazarus Godson; Manova, Stephen; Larkins, Jeffry Pon

doi:10.1007/978-981-16-4083-4_42

Cited by 4 publications

(1 citation statement)

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“…The attribute evaluator, the influence of the number of features, and Decision Tree (DT) are the three most important methods for selecting the features. DT utilizes a graphical structure to select the most significant features [21]. The attribute evaluator calculates the value of a characteristic subset by studying each feature's analytical capacity and severance level.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers

Gnanasekaran,

Jakkamputi,

Rakkiyannan

et al. 2023

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%