{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:35:50Z","timestamp":1782405350163,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Railway switches and crossings (S&amp;Cs) are critical, high-value assets in railway networks. A single failure of such an asset could result in severe network disturbance and considerable economical losses. Squats are common rail surface defects of S&amp;Cs and need to be detected and estimated at an early stage to minimise maintenance costs and increase the reliability of S&amp;Cs. For practicality, installation of wired or wireless sensors along the S&amp;C may not be reliable due to the risk of damages of power and signal cables or sensors. To cope with these issues, this study presents a method for collecting and processing vibration data from an accelerometer installed at the point machine to extract features related to the squat defects of the S&amp;C. An unsupervised anomaly-detection method using the isolation forest algorithm is applied to generate anomaly scores from the features. Important features are ranked and selected. This paper describes the procedure of parameter tuning and presents the achieved anomaly scores. The results show that the proposed method is effective and that the generated anomaly scores indicate the health status of an S&amp;C regarding squat defects.<\/jats:p>","DOI":"10.3390\/s22176357","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T23:48:58Z","timestamp":1661384938000},"page":"6357","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Squat Detection of Railway Switches and Crossings Using Wavelets and Isolation Forest"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7669-6815","authenticated-orcid":false,"given":"Yang","family":"Zuo","sequence":"first","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0318-6157","authenticated-orcid":false,"given":"Florian","family":"Thiery","sequence":"additional","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2300-9716","authenticated-orcid":false,"given":"Praneeth","family":"Chandran","sequence":"additional","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0216-5058","authenticated-orcid":false,"given":"Johan","family":"Odelius","sequence":"additional","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matti","family":"Rantatalo","sequence":"additional","affiliation":[{"name":"Division of Operation and Maintenance Engineering, Lule\u00e5 University of Technology, 97187 Lule\u00e5, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chandran, P. (2021). An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning. Sustainability, 13.","DOI":"10.3390\/su132112051"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Silva, R., Ribeiro, D., Bragan\u00e7a, C., Costa, C., Ar\u00eade, A., and Cal\u00e7ada, R. (2021). Model Updating of a Freight Wagon Based on Dynamic Tests under Different Loading Scenarios. Appl. Sci., 11.","DOI":"10.3390\/app112210691"},{"key":"ref_3","first-page":"1","article-title":"Maintenance analysis for continuous improvement of railway infrastructure performance","volume":"11","author":"Famurewa","year":"2014","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1080\/00423110500233487","article-title":"Simulation of dynamic interaction between train and railway turnout","volume":"44","author":"Kassa","year":"2006","journal-title":"Veh. Syst. Dyn."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hamadache, M., Dutta, S., Olaby, O., Ambur, R., Stewart, E., and Dixon, R. (2019). On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview. Appl. Sci., 9.","DOI":"10.3390\/app9235129"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1177\/0954409715624723","article-title":"Monitoring of strain of in-service railway switch rails through field experimentation","volume":"230","author":"Cornish","year":"2016","journal-title":"Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit"},{"key":"ref_7","unstructured":"Administration, S.T. (2018). Trafikverkets \u00c5rsredovisning, Annual Report, Trafikverket. Technical Report."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.measurement.2018.07.062","article-title":"Experimental tools for railway crossing condition monitoring (crossing condition monitoring tools)","volume":"129","author":"Liu","year":"2018","journal-title":"Measurement"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.measurement.2018.04.094","article-title":"In situ measurements of the crossing vibrations of a railway turnout","volume":"125","author":"Boogaard","year":"2018","journal-title":"Measurement"},{"key":"ref_10","first-page":"601","article-title":"Prognosis of railway ballast degradation for turnouts using track-side accelerations","volume":"234","author":"Barkhordari","year":"2020","journal-title":"Proc. Inst. Mech. Eng. Part O J. Risk Reliab."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Milosevic, M.D.G., P\u00e5lsson, B.A., Nissen, A., Nielsen, J.C.O., and Johansson, H. (2022). Condition Monitoring of Railway Crossing Geometry via Measured and Simulated Track Responses. Sensors, 22.","DOI":"10.3390\/s22031012"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kerrouche, A., Najeh, T., and Jaen-Sola, P. (2021). Experimental Strain Measurement Approach Using Fiber Bragg Grating Sensors for Monitoring of Railway Switches and Crossings. Sensors, 21.","DOI":"10.3390\/s21113639"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wei, Z., N\u00fa\u00f1ez, A., Li, Z., and Dollevoet, R. (2017). Evaluating Degradation at Railway Crossings Using Axle Box Acceleration Measurements. Sensors, 17.","DOI":"10.3390\/s17102236"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104987","DOI":"10.1016\/j.engfailanal.2020.104987","article-title":"Observed failures at railway turnouts: Failure analysis, possible causes and links to current and future research","volume":"119","author":"Grossoni","year":"2021","journal-title":"Eng. Fail. Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1177\/0954409714523583","article-title":"Parametric study of axle box acceleration at squats","volume":"229","author":"Molodova","year":"2015","journal-title":"Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1109\/TITS.2014.2307955","article-title":"Automatic Detection of Squats in Railway Infrastructure","volume":"15","author":"Molodova","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cho, H., and Park, J. (2021). Study of Rail Squat Characteristics through Analysis of Train Axle Box Acceleration Frequency. Appl. Sci., 11.","DOI":"10.3390\/app11157022"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hamadache, M., Dutta, S., Ambur, R., Olaby, O., Stewart, E., and Dixon, R. (2019, January 15\u201318). Residual-based Fault Detection Method: Application to Railway Switch & Crossing (S&C) System. Proceedings of the 2019 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea.","DOI":"10.23919\/ICCAS47443.2019.8971747"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1080\/00423114.2019.1610181","article-title":"Squats and corrugation detection of railway track based on time-frequency analysis by using bogie acceleration measurements","volume":"58","author":"Wei","year":"2019","journal-title":"Veh. Syst. Dyn."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1051\/meca\/2018051","article-title":"Fault detection of damper in railway vehicle suspension based on the cross-correlation analysis of bogie accelerations","volume":"20","author":"Dumitriu","year":"2019","journal-title":"Mech. Ind."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Malekjafarian, A., OBrien, E.J., Quirke, P., Cantero, D., and Golpayegani, F. (2021). Railway track loss-of-stiffness detection using bogie filtered displacement data measured on a passing train. Infrastructures, 6.","DOI":"10.3390\/infrastructures6060093"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1016\/j.apacoust.2004.04.003","article-title":"Railway wheel fault diagnosis using a fuzzy-logic method","volume":"65","author":"Skarlatos","year":"2004","journal-title":"Appl. Acoust."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1016\/j.ymssp.2005.12.012","article-title":"Wheel-flat diagnostic tool via wavelet transform","volume":"20","author":"Belotti","year":"2006","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Van Esbeen, B., Finet, C., Vandebrouck, R., Kinet, D., Boelen, K., Guyot, C., Kouroussis, G., and Caucheteur, C. (2022). Smart Railway Traffic Monitoring Using Fiber Bragg Grating Strain Gauges. Sensors, 22.","DOI":"10.1117\/12.2624778"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mosleh, A., Montenegro, P.A., Costa, P.A., and Cal\u00e7ada, R. (2021). Railway vehicle wheel flat detection with multiple records using spectral kurtosis analysis. Appl. Sci., 11.","DOI":"10.3390\/app11094002"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Najeh, T., Lundberg, J., and Kerrouche, A. (2021). Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings. Sensors, 21.","DOI":"10.3390\/s21155217"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1109\/TIE.2015.2389761","article-title":"Improvements in axle box acceleration measurements for the detection of light squats in railway infrastructure","volume":"62","author":"Li","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, Z., Molodova, M., Zhao, X., and Dollevoet, R. (2010, January 24\u201327). Squat treatment by way of minimum action based on early detection to reduce life cycle costs. Proceedings of the Joint Rail Conference, Urbana, IL, USA.","DOI":"10.1115\/JRC2010-36184"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/S0888-3270(03)00075-X","article-title":"Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography","volume":"18","author":"Peng","year":"2004","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"40347","DOI":"10.1109\/ACCESS.2020.2975875","article-title":"Wavelet Denoising for the Vibration Signals of Wind Turbines Based on Variational Mode Decomposition and Multiscale Permutation Entropy","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.measurement.2019.05.049","article-title":"Application of a new EWT-based denoising technique in bearing fault diagnosis","volume":"144","author":"Chegini","year":"2019","journal-title":"Measurement"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ijleo.2018.12.074","article-title":"Application of distributed acoustic sensor technology in train running condition monitoring of the heavy-haul railway","volume":"181","author":"He","year":"2019","journal-title":"Optik"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luo, G., and Zhang, D. (2012). Wavelet Denoising, IntechOpen. Chapter 4.","DOI":"10.5772\/37424"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1177\/1077546311412992","article-title":"Performance of wavelet denoising in vibration analysis: Highlighting","volume":"18","author":"Chiementin","year":"2012","journal-title":"J. Vib. Control"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","article-title":"A Practical Guide to Wavelet Analysis","volume":"79","author":"Torrence","year":"1998","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1137\/1037142","article-title":"A Friendly Guide to Wavelets (Gerald Kaiser)","volume":"37","author":"Frazier","year":"1995","journal-title":"SIAM Rev."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.H. (2008, January 15\u201319). Isolation forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Weiss, Y., Sch\u00f6lkopf, B., and Platt, J. (2006). Laplacian Score for Feature Selection. Advances in Neural Information Processing Systems, Proceedings of the 19th Annual Conference on Neural Information Processing Systems (NIPS 2005), Vancouver, BC, Canada, 5\u20138 December 2005, MIT Press.","DOI":"10.7551\/mitpress\/7503.001.0001"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1145\/2347736.2347755","article-title":"A few useful things to know about machine learning","volume":"55","author":"Domingos","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e1379","DOI":"10.1002\/widm.1379","article-title":"Interpretability of machine learning-based prediction models in healthcare","volume":"10","author":"Stiglic","year":"2020","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6357\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:25Z","timestamp":1760141665000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,24]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176357"],"URL":"https:\/\/doi.org\/10.3390\/s22176357","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,24]]}}}