Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework
Brake failure is a critical safety issue in the automotive industry, often resulting from mechanical wear, hydraulic malfunctions, or electronic failure. Common causes include brake fluid leakage, worn-out brake pads, and damaged brake lines, exacerbated by poor maintenance and ignored warning signs...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Cogent Engineering |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2025.2549368 |
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| _version_ | 1849229093736808448 |
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| author | Saumye Saran Das Raushan Kumar Manas Ranjan Prusty Tapan K. Mahanta Subhra Rani Patra |
| author_facet | Saumye Saran Das Raushan Kumar Manas Ranjan Prusty Tapan K. Mahanta Subhra Rani Patra |
| author_sort | Saumye Saran Das |
| collection | DOAJ |
| description | Brake failure is a critical safety issue in the automotive industry, often resulting from mechanical wear, hydraulic malfunctions, or electronic failure. Common causes include brake fluid leakage, worn-out brake pads, and damaged brake lines, exacerbated by poor maintenance and ignored warning signs, such as squealing. Effective fault detection is essential to ensure road safety. This study is motivated by the critical need to enhance the accuracy of brake fault detection and classification, which is a key factor in improving road safety and reducing the risk of accidents caused by brake system failures. This paper presents an automated approach for detecting hydraulic brake faults by analyzing five types of brake sound anomalies, Type I to Type V, covering various disk pad wear scenarios. These time-domain sound signals are converted to frequency-domain image representation using a mel spectrogram, and a custom 14 layer Deep Sequential Convolutional Neural Network (CNN) model classifies these images. The model achieved 97.7% accuracy in binary classification and 97.8% in 5-class classification, with perfect results at 20 epochs during hold-out cross-validation. This study significantly enhances the safety of hydraulic brake systems by automating fault detection and classification using a deep sequential CNN framework. |
| format | Article |
| id | doaj-art-5bc846a0096f48ad8a73132d8218b9fa |
| institution | Kabale University |
| issn | 2331-1916 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj-art-5bc846a0096f48ad8a73132d8218b9fa2025-08-22T06:52:35ZengTaylor & Francis GroupCogent Engineering2331-19162025-12-0112110.1080/23311916.2025.2549368Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning frameworkSaumye Saran Das0Raushan Kumar1Manas Ranjan Prusty2Tapan K. Mahanta3Subhra Rani Patra4School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Computer Science and Engineering, Vellore Institute of Technology, Chennai, IndiaCentre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, IndiaSchool of Mechanical Engineering, Vellore Institute of Technology, Chennai, IndiaInformation Systems and Operations Management, University of Texas at Arlington, TX, USABrake failure is a critical safety issue in the automotive industry, often resulting from mechanical wear, hydraulic malfunctions, or electronic failure. Common causes include brake fluid leakage, worn-out brake pads, and damaged brake lines, exacerbated by poor maintenance and ignored warning signs, such as squealing. Effective fault detection is essential to ensure road safety. This study is motivated by the critical need to enhance the accuracy of brake fault detection and classification, which is a key factor in improving road safety and reducing the risk of accidents caused by brake system failures. This paper presents an automated approach for detecting hydraulic brake faults by analyzing five types of brake sound anomalies, Type I to Type V, covering various disk pad wear scenarios. These time-domain sound signals are converted to frequency-domain image representation using a mel spectrogram, and a custom 14 layer Deep Sequential Convolutional Neural Network (CNN) model classifies these images. The model achieved 97.7% accuracy in binary classification and 97.8% in 5-class classification, with perfect results at 20 epochs during hold-out cross-validation. This study significantly enhances the safety of hydraulic brake systems by automating fault detection and classification using a deep sequential CNN framework.https://www.tandfonline.com/doi/10.1080/23311916.2025.2549368Hydraulic disc brake systemmulti-class brake soundfrequency domain filter bankmel-spectrogramdeep sequential modelsMechanical Engineering |
| spellingShingle | Saumye Saran Das Raushan Kumar Manas Ranjan Prusty Tapan K. Mahanta Subhra Rani Patra Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework Cogent Engineering Hydraulic disc brake system multi-class brake sound frequency domain filter bank mel-spectrogram deep sequential models Mechanical Engineering |
| title | Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| title_full | Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| title_fullStr | Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| title_full_unstemmed | Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| title_short | Hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| title_sort | hydraulic brake fault detection from acoustic signals using frequency domain filter bank integrated deep learning framework |
| topic | Hydraulic disc brake system multi-class brake sound frequency domain filter bank mel-spectrogram deep sequential models Mechanical Engineering |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2025.2549368 |
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