{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:56:26Z","timestamp":1780379786142,"version":"3.54.1"},"reference-count":55,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program","award":["IITP-2021-2016-0-00313"],"award-info":[{"award-number":["IITP-2021-2016-0-00313"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning","award":["2017R1E1A1A01074345"],"award-info":[{"award-number":["2017R1E1A1A01074345"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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%.<\/jats:p>","DOI":"10.3390\/s21186221","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"6221","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["A Novel Approach to Railway Track Faults Detection Using Acoustic Analysis"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-7043","authenticated-orcid":false,"given":"Rahman","family":"Shafique","sequence":"first","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0671-2060","authenticated-orcid":false,"given":"Hafeez-Ur-Rehman","family":"Siddiqui","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3747-1263","authenticated-orcid":false,"given":"Saleem","family":"Ullah","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science and Information Technology, Khawaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Abubakar","family":"Siddique","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Technology, Ghazi University, Dera Ghazi Khan 32201, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1209-8565","authenticated-orcid":false,"given":"Ernesto","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Broward College, Broward Count, FL 33332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8271-6496","authenticated-orcid":false,"given":"Imran","family":"Ashraf","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6431-5357","authenticated-orcid":false,"given":"Sandra","family":"Dudley","sequence":"additional","affiliation":[{"name":"School of Engineering and Design, London South Bank University, London SE1 0AA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"ref_1","unstructured":"Apking, A. (2018). Worldwide Market for Railway Industries Study: Market Volumes for OEM Business and After-Sales Service as Well as Prospects for Market Developments of Infrastructure and Rolling Stock, SCI Verkehr GmbH."},{"key":"ref_2","unstructured":"Qureshi, N. (2021, August 01). Pakistan Railways Achieves Record Income in 2018\u20132019. Available online: https:\/\/www.railjournal.com\/news\/pakistan-railways-achieves-record-income-in-2018-19\/."},{"key":"ref_3","unstructured":"Auditor General of Pakistan (2018). Audit Report on the Accounts of Pakistan Railways Audit Year 2019\u201320."},{"key":"ref_4","unstructured":"Majeed, A. (2020, September 02). Train Accident. Available online: https:\/\/www.pakistantoday.com.pk\/tag\/train-accident\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.trc.2013.01.008","article-title":"An algorithm for improved performance of railway condition monitoring equipment: Alternating-current point machine case study","volume":"30","author":"Asada","year":"2013","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nelwamondo, F.V., and Marwala, T. (2006, January 8\u201311). Faults detection using gaussian mixture models, mel-frequency cepstral coefficients and kurtosis. Proceedings of the 2006 IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan.","DOI":"10.1109\/ICSMC.2006.384397"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"401","DOI":"10.35940\/ijeat.E7811.088619","article-title":"Arduino Based Programmed Railway Track Crack Monitoring Vehicle","volume":"8","author":"Manoj","year":"2019","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_8","first-page":"945","article-title":"An Arduino based Method for Detecting Cracks and Obstacles in Railway Tracks","volume":"4","author":"Agrawal","year":"2018","journal-title":"Int. J. Sci. Res. Sci. Eng. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Marich, S. (2009). 27-Managing the Wheel\u2013Rail Interface: The Australian Experience, Woodhead Publishing.","DOI":"10.1016\/B978-1-84569-412-8.50027-6"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.trgeo.2018.09.002","article-title":"Monitoring and repair of isolated trackbed defects on a ballasted railway","volume":"17","author":"Milne","year":"2018","journal-title":"Transp. Geotech."},{"key":"ref_11","unstructured":"(2020, September 02). Detection of Missing Nuts & Bolts on Rail Fishplate. Available online: https:\/\/www.semanticscholar.org\/paper\/Detection-of-missing-nuts-%26-bolts-on-rail-fishplate-Jaffery-Sharma\/3cb091d98934c75d40b376bdee905869c78c23ab."},{"key":"ref_12","first-page":"550","article-title":"William Scoresby Junior (1789\u20131857)","volume":"35","author":"Stamp","year":"1982","journal-title":"Arctic"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.icte.2017.11.010","article-title":"Vision based rail track extraction and monitoring through drone imagery","volume":"5","author":"Singh","year":"2019","journal-title":"ICT Express"},{"key":"ref_14","unstructured":"Ritika, S., and Rao, D. (2018). Data Augmentation of Railway Images for Track Inspection. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"James, A., Jie, W., Xulei, Y., Chenghao, Y., Ngan, N.B., Yuxin, L., Yi, S., Chandrasekhar, V., and Zeng, Z. (2018, January 12\u201314). Tracknet\u2014A deep learning based fault detection for railway track inspection. Proceedings of the 2018 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Singapore.","DOI":"10.1109\/ICIRT.2018.8641608"},{"key":"ref_16","first-page":"82","article-title":"Railway track fault detection system by using ir sensors and bluetooth technology","volume":"1","author":"Krishna","year":"2017","journal-title":"Asian J. Appl. Sci. Technol. (AJAST)"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rser.2015.03.002","article-title":"Reducing energy demand: A review of issues, challenges and approaches","volume":"47","author":"Sorrell","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_18","unstructured":"(2020, September 02). Fotos Gratis: Madera, Pista, Ferrocarril, Carril, Transporte, Lastre, v\u00eda, Traviesas, v\u00edas del Tren 4390x3292 836877-Imagenes-Gratis-PxHere. Available online: https:\/\/www.google.com\/imgres?imgurl=https%3A%2F%2Fget.pxhere.co%2Fphoto%2Fwood-track-railway-rail-transport-ballast-via-sleepers-train-tracks-836877.jpg&imgrefurl=https%3A%2F%2Fpxhere.com%2Fes%2Fphoto%2F836877&tbnid=YMh0z2b1N9ZEJM&vet=12ahUKEwj_xJnaicrrAhUEgM4BHYjJCbEQMygAegQIARAX..i&docid=ef5bG3BaL9B8bM&w=4390&h=3292&q=railway%20ballast%20meaning&hl=en-PK&ved=2ahUKEwj_xJnaicrrAhUEgM4BHYjJCbEQMygAegQIARAX."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1109\/TIM.2013.2283741","article-title":"Automatic fastener classification and defect detection in vision-based railway inspection systems","volume":"63","author":"Feng","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_20","unstructured":"Mishra, U., Gupta, V., Ahzam, S.M., and Tripathi, S.M. (2021, August 01). Google Maps Based Railway Track Fault Detection Over Internet. Available online: https:\/\/www.ripublication.com\/Volume\/ijaerv14n2.htm."},{"key":"ref_21","first-page":"2395-0056","article-title":"Detection of Crack In Rail Road Using Ultrasonic and PIR Sensor","volume":"4","author":"Nagdevte","year":"2017","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"ref_22","unstructured":"(2020, September 02). Definition of LIMIT SWITCH. Available online: https:\/\/www.merriam-webster.com\/dictionary\/limit%20switch."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"052045","DOI":"10.1088\/1757-899X\/263\/5\/052045","article-title":"Automated vehicle for railway track fault detection","volume":"Volume 263","author":"Bhushan","year":"2017","journal-title":"Proceedings of the IOP Conference Series: Materials Science and Engineering"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lee, J., Choi, H., Park, D., Chung, Y., Kim, H.Y., and Yoon, S. (2016). Fault detection and diagnosis of railway point machines by sound analysis. Sensors, 16.","DOI":"10.3390\/s16040549"},{"key":"ref_25","unstructured":"Arivazhagan, S., Shebiah, R.N., Magdalene, J.S., and Sushmitha, G. (2015). Railway Track Derailment Inspection System Using Segmentation Based Fractal Texture Analysis. ICTACT J. Image Video Process., 6."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-017-0241-y","article-title":"Real time detection system for rail surface defects based on machine vision","volume":"2018","author":"Min","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_27","first-page":"21476","article-title":"Implementation of railway track crack detection and protection","volume":"6","author":"Karthick","year":"2017","journal-title":"Int. J. Eng. Comput. Sci. (IJECS)"},{"key":"ref_28","unstructured":"(2020, November 02). Sony ECM-X7BMP Electret Condenser Lavalier Microphone for UWP Transmitters. Available online: https:\/\/www.bhphotovideo.com\/c\/product\/608509-REG\/Sony_ECM_X7BMP_ECM_X7BMP_Electret_Condenser_Lavalier.html."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chauhan, P.M., and Desai, N.P. (2014, January 6\u20138). Mel frequency cepstral coefficients (MFCC) based speaker identification in noisy environment using wiener filter. Proceedings of the 2014 IEEE International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India.","DOI":"10.1109\/ICGCCEE.2014.6921394"},{"key":"ref_30","first-page":"19","article-title":"MFCC and its applications in speaker recognition","volume":"1","author":"Tiwari","year":"2010","journal-title":"Int. J. Emerg. Technol."},{"key":"ref_31","first-page":"13","article-title":"MFCC dan KNN untuk Pengenalan Suara Artikulasi P","volume":"2","author":"Anggoro","year":"2020","journal-title":"Aviat. Electron. Inf. Technol. Telecommun. Electr. Control."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hossan, M.A., Memon, S., and Gregory, M.A. (2010, January 13\u201315). A novel approach for MFCC feature extraction. Proceedings of the 2010 IEEE 4th International Conference on Signal Processing and Communication Systems, Gold Coast, QLD, Australia.","DOI":"10.1109\/ICSPCS.2010.5709752"},{"key":"ref_33","unstructured":"Alim, S.A., and Rashid, N.K.A. (2018). Some Commonly Used Speech Feature Extraction Algorithms, IntechOpen."},{"key":"ref_34","unstructured":"Schulte-Werning, B., Thompson, D., Gautier, P.E., Hanson, C., Hemsworth, B., Nelson, J., Maeda, T., and de Vos, P. (2008). Noise and Vibration Mitigation for Rail Transportation Systems: Proceedings of the 9th International Workshop on Railway Noise, Munich, Germany, 4\u20138 September 2007, Springer Science & Business Media. Notes on Numerical."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shabana, A.A., and Ling, H. (2020). Characterization and quantification of railroad spiral-joint discontinuities. Mech. Based Des. Struct. Mach., 1\u201326.","DOI":"10.1080\/15397734.2020.1855193"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.ssci.2018.03.022","article-title":"Track circuit reliability assessment for preventing railway accidents","volume":"110","author":"Wybo","year":"2018","journal-title":"Saf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.ssci.2015.06.017","article-title":"Fault tree analysis combined with quantitative analysis for high-speed railway accidents","volume":"79","author":"Liu","year":"2015","journal-title":"Saf. Sci."},{"key":"ref_38","unstructured":"U.S. Department of Transportation (2007). A Survey of Wheel\/Rail Friction, Technical Report."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chakroborty, S., Roy, A., and Saha, G. (2006, January 15\u201317). Fusion of a complementary feature set with MFCC for improved closed set text-independent speaker identification. Proceedings of the 2006 IEEE International Conference on Industrial Technology, Mumbai, India.","DOI":"10.1109\/ICIT.2006.372388"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"De Lara, J.R.C. (2005). A method of automatic speaker recognition using cepstral features and vectorial quantization. Iberoamerican Congress on Pattern Recognition, Springer.","DOI":"10.1007\/11578079_16"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and binary SVM classification: Implications for training and classification users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1207\/S15324796ABM2603_02","article-title":"Classification and regression tree analysis in public health: Methodological review and comparison with logistic regression","volume":"26","author":"Lemon","year":"2003","journal-title":"Ann. Behav. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.eij.2012.08.002","article-title":"Support vector machines (SVMs) versus multilayer perception (MLP) in data classification","volume":"13","author":"Zanaty","year":"2012","journal-title":"Egypt. Inform. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s00432-018-02834-7","article-title":"Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm","volume":"145","author":"Jeyaraj","year":"2019","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"30234","DOI":"10.1109\/ACCESS.2020.2972632","article-title":"Classification of shopify app user reviews using novel multi text features","volume":"8","author":"Rustam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_46","unstructured":"Deng, X.B., Ye, Y.M., Li, H.B., and Huang, J.Z. (2008, January 12\u201315). An improved random forest approach for detection of hidden web search interfaces. Proceedings of the 2008 IEEE International Conference on Machine Learning and Cybernetics, Kunming, China."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., and Park, Y. (2018). MagIO: Magnetic field strength based indoor-outdoor detection with a commercial smartphone. Micromachines, 9.","DOI":"10.3390\/mi9100534"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TKDE.2013.34","article-title":"Decision trees for mining data streams based on the gaussian approximation","volume":"26","author":"Rutkowski","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mesaros, A., Heittola, T., Dikmen, O., and Virtanen, T. (2015, January 19\u201324). Sound event detection in real life recordings using coupled matrix factorization of spectral representations and class activity annotations. Proceedings of the 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP), South Brisbane, QLD, Australia.","DOI":"10.1109\/ICASSP.2015.7177950"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ashraf, I., Hur, S., and Park, Y. (2019). Application of deep convolutional neural networks and smartphone sensors for indoor localization. Appl. Sci., 9.","DOI":"10.3390\/app9112337"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3390\/informatics7030023","article-title":"Improving smart cities safety using sound events detection based on deep neural network algorithms","volume":"7","author":"Ciaburro","year":"2020","journal-title":"Informatics"},{"key":"ref_53","unstructured":"Lee, D., Lee, S., Han, Y., and Lee, K. (2020, September 02). Ensemble of convolutional neural networks for weakly-supervised sound event detection using multiple scale input. In Detection and Classification of Acoustic Scenes and Events (DCASE); Munich, Germany. Available online: http:\/\/dcase.community\/documents\/challenge2017\/technical_reports\/DCASE2017_Lee_199.pdf."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Reshi, A.A., Rustam, F., Mehmood, A., Alhossan, A., Alrabiah, Z., Ahmad, A., Alsuwailem, H., and Choi, G.S. (2021). An Efficient CNN Model for COVID-19 Disease Detection Based on X-ray Image Classification. Complexity, 2021.","DOI":"10.1155\/2021\/6621607"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Rustam, F., Ashraf, I., Mehmood, A., Ullah, S., and Choi, G.S. (2019). Tweets classification on the base of sentiments for US airline companies. Entropy, 21.","DOI":"10.3390\/e21111078"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6221\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:57Z","timestamp":1760166057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,16]]},"references-count":55,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21186221"],"URL":"https:\/\/doi.org\/10.3390\/s21186221","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,16]]}}}