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Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data
Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Auto...
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/PMC10460052/ https://www.ncbi.nlm.nih.gov/pubmed/37631555 http://dx.doi.org/10.3390/s23167018 |
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author | Rustam, Furqan Ishaq, Abid Hashmi, Muhammad Shadab Alam Siddiqui, Hafeez Ur Rehman López, Luis Alonso Dzul Galán, Juan Castanedo Ashraf, Imran |
author_facet | Rustam, Furqan Ishaq, Abid Hashmi, Muhammad Shadab Alam Siddiqui, Hafeez Ur Rehman López, Luis Alonso Dzul Galán, Juan Castanedo Ashraf, Imran |
author_sort | Rustam, Furqan |
collection | PubMed |
description | Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. |
format | Online Article Text |
id | pubmed-10460052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104600522023-08-27 Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data Rustam, Furqan Ishaq, Abid Hashmi, Muhammad Shadab Alam Siddiqui, Hafeez Ur Rehman López, Luis Alonso Dzul Galán, Juan Castanedo Ashraf, Imran Sensors (Basel) Article Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings. MDPI 2023-08-08 /pmc/articles/PMC10460052/ /pubmed/37631555 http://dx.doi.org/10.3390/s23167018 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 Rustam, Furqan Ishaq, Abid Hashmi, Muhammad Shadab Alam Siddiqui, Hafeez Ur Rehman López, Luis Alonso Dzul Galán, Juan Castanedo Ashraf, Imran Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title | Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title_full | Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title_fullStr | Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title_full_unstemmed | Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title_short | Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data |
title_sort | railway track fault detection using selective mfcc features from acoustic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10460052/ https://www.ncbi.nlm.nih.gov/pubmed/37631555 http://dx.doi.org/10.3390/s23167018 |
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