A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis
Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive inves...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6221 |
| _version_ | 1827681234044583936 |
|---|---|
| author | Rahman Shafique Hafeez-Ur-Rehman Siddiqui Furqan Rustam Saleem Ullah Muhammad Abubakar Siddique Ernesto Lee Imran Ashraf Sandra Dudley |
| author_facet | Rahman Shafique Hafeez-Ur-Rehman Siddiqui Furqan Rustam Saleem Ullah Muhammad Abubakar Siddique Ernesto Lee Imran Ashraf Sandra Dudley |
| author_sort | Rahman Shafique |
| collection | DOAJ |
| description | Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%. |
| first_indexed | 2024-03-10T07:13:19Z |
| format | Article |
| id | doaj.art-3df7af45f0134145b5eac023c26115e7 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:19Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3df7af45f0134145b5eac023c26115e72023-11-22T15:13:34ZengMDPI AGSensors1424-82202021-09-012118622110.3390/s21186221A Novel Approach to Railway Track Faults Detection Using Acoustic AnalysisRahman Shafique0Hafeez-Ur-Rehman Siddiqui1Furqan Rustam2Saleem Ullah3Muhammad Abubakar Siddique4Ernesto Lee5Imran Ashraf6Sandra Dudley7Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanFaculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanFaculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanFaculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanDepartment of Computer Science and Information Technology, Ghazi University, Dera Ghazi Khan 32201, PakistanDepartment of Computer Science, Broward College, Broward Count, FL 33332, USADepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, KoreaSchool of Engineering and Design, London South Bank University, London SE1 0AA, UKRegular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.https://www.mdpi.com/1424-8220/21/18/6221railway track inspectionacoustic signals analysisrailway track cracks detectionmachine learningdeep convolution neural networkslogistic regression |
| spellingShingle | Rahman Shafique Hafeez-Ur-Rehman Siddiqui Furqan Rustam Saleem Ullah Muhammad Abubakar Siddique Ernesto Lee Imran Ashraf Sandra Dudley A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis Sensors railway track inspection acoustic signals analysis railway track cracks detection machine learning deep convolution neural networks logistic regression |
| title | A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis |
| title_full | A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis |
| title_fullStr | A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis |
| title_full_unstemmed | A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis |
| title_short | A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis |
| title_sort | novel approach to railway track faults detection using acoustic analysis |
| topic | railway track inspection acoustic signals analysis railway track cracks detection machine learning deep convolution neural networks logistic regression |
| url | https://www.mdpi.com/1424-8220/21/18/6221 |
| work_keys_str_mv | AT rahmanshafique anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT hafeezurrehmansiddiqui anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT furqanrustam anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT saleemullah anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT muhammadabubakarsiddique anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT ernestolee anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT imranashraf anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT sandradudley anovelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT rahmanshafique novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT hafeezurrehmansiddiqui novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT furqanrustam novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT saleemullah novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT muhammadabubakarsiddique novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT ernestolee novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT imranashraf novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis AT sandradudley novelapproachtorailwaytrackfaultsdetectionusingacousticanalysis |