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A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675424/ https://www.ncbi.nlm.nih.gov/pubmed/38005482 http://dx.doi.org/10.3390/s23229093 |
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author | Gnanasekaran, Sakthivel Jakkamputi, Lakshmi Pathi Rakkiyannan, Jegadeeshwaran Thangamuthu, Mohanraj Bhalerao, Yogesh |
author_facet | Gnanasekaran, Sakthivel Jakkamputi, Lakshmi Pathi Rakkiyannan, Jegadeeshwaran Thangamuthu, Mohanraj Bhalerao, Yogesh |
author_sort | Gnanasekaran, Sakthivel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10675424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106754242023-11-10 A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers Gnanasekaran, Sakthivel Jakkamputi, Lakshmi Pathi Rakkiyannan, Jegadeeshwaran Thangamuthu, Mohanraj Bhalerao, Yogesh Sensors (Basel) Article 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. MDPI 2023-11-10 /pmc/articles/PMC10675424/ /pubmed/38005482 http://dx.doi.org/10.3390/s23229093 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gnanasekaran, Sakthivel Jakkamputi, Lakshmi Pathi Rakkiyannan, Jegadeeshwaran Thangamuthu, Mohanraj Bhalerao, Yogesh A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title | A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title_full | A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title_fullStr | A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title_full_unstemmed | A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title_short | A Comprehensive Approach for Detecting Brake Pad Defects Using Histogram and Wavelet Features with Nested Dichotomy Family Classifiers |
title_sort | comprehensive approach for detecting brake pad defects using histogram and wavelet features with nested dichotomy family classifiers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675424/ https://www.ncbi.nlm.nih.gov/pubmed/38005482 http://dx.doi.org/10.3390/s23229093 |
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