{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:56:52Z","timestamp":1775145412532,"version":"3.50.1"},"reference-count":48,"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\/"}],"funder":[{"DOI":"10.13039\/501100007835","name":"Silesian University of Technology","doi-asserted-by":"publisher","award":["BKM-689\/RT4\/2022, 12\/040\/BKM22\/0048"],"award-info":[{"award-number":["BKM-689\/RT4\/2022, 12\/040\/BKM22\/0048"]}],"id":[{"id":"10.13039\/501100007835","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Micro-electromechanical-systems (MEMS) based sensors are used for monitoring the state of machines in condition-based maintenance tasks. This approach is applied at tram depots for the purpose of identifying faulty wheels on trams in order to eliminate defective trams at the entry or dispatch gates. The application of MEMS-based sensors for the detection of wheel faults is the focus of this study. A method for processing of the collected sensor data is developed. It is based on assessing the energy of vibrations at different frequency bands. Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) is used for obtaining a description of the sensor data. The task of finding the energy threshold for detecting faulty wheels, frequency band and parameters of MODWPT which most distinctly distinguish the wheels is the goal of the method. The weighted difference (DW) between the extreme values of energy in a frequency band for normal and faulty wheels is proposed as the measure of the ability to distinguish the wheels. The search for the solution is formulated as a discrete optimisation problem of maximising this measure. Both the simulation and experimental results indicate that faulty wheels have greater vibration energy than normal wheels. The properties of this approach are discussed and evaluated.<\/jats:p>","DOI":"10.3390\/s22176373","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T23:48:58Z","timestamp":1661384938000},"page":"6373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detection of Tram Wheel Faults Using MEMS-Based Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2470-7153","authenticated-orcid":false,"given":"Yohanis Dabesa","family":"Jelila","sequence":"first","affiliation":[{"name":"Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland"},{"name":"Faculty of Mechanical Engineering, Jimma Institute of Technology, Jimma University, Jimma P.O. Box 378, Ethiopia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9792-6528","authenticated-orcid":false,"given":"Wies\u0142aw","family":"Pamu\u0142a","sequence":"additional","affiliation":[{"name":"Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103312","DOI":"10.1016\/j.dsp.2021.103312","article-title":"Fault Diagnosis of Train Rotating Parts Based on Multi-Objective VMD Optimization and Ensemble Learning","volume":"121","author":"Jin","year":"2022","journal-title":"Digit. Signal Process. Rev. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.wear.2015.02.018","article-title":"A Numerical Analysis of the Contact Stress Distribution and Physical Modelling of Abrasive Wear in the Tram Wheel-Frog System","volume":"328\u2013329","author":"Kuminek","year":"2015","journal-title":"Wear"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106365","DOI":"10.1016\/j.triboint.2020.106365","article-title":"Characteristics of Tram Wheel Wear: Focus on Mechanism Identification and Surface Topography","volume":"150","author":"Wojciechowski","year":"2020","journal-title":"Tribol. Int."},{"key":"ref_4","first-page":"1","article-title":"Operational problems of tramway infrastructure in sharp curves","volume":"118","author":"Rochel","year":"2021","journal-title":"Tech. Trans."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105118","DOI":"10.1109\/ACCESS.2020.3000068","article-title":"Intelligent Diagnosis for Railway Wheel Flat Using Frequency-Domain Gramian Angular Field and Transfer Learning Network","volume":"8","author":"Bai","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1177\/0954409716656218","article-title":"Condition Monitoring Approaches for the Detection of Railway Wheel Defects","volume":"231","author":"Alemi","year":"2017","journal-title":"Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TITS.2014.2366512","article-title":"Wireless Sensor Networks for Condition Monitoring in the Railway Industry: A Survey","volume":"16","author":"Hodge","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/JSEN.2018.2875160","article-title":"Onboard Condition Monitoring Sensors, Systems and Techniques for Freight Railway Vehicles: A Review","volume":"19","author":"Bernal","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1177\/0954409719831822","article-title":"Perspectives on Railway Axle Bearing Condition Monitoring","volume":"234","author":"Entezami","year":"2020","journal-title":"Proc. Inst. Mech. Eng. Part F J. Rail Rapid Transit"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"116965","DOI":"10.1016\/j.jsv.2022.116965","article-title":"An Adaptive Graph Morlet Wavelet Transform for Railway Wayside Acoustic Detection","volume":"529","author":"Zhang","year":"2022","journal-title":"J. Sound Vib."},{"key":"ref_12","unstructured":"(2022, July 25). Choosing the Most Suitable MEMS Accelerometer for Your Application\u2014Part 1|Analog Devices. Available online: https:\/\/www.analog.com\/en\/analog-dialogue\/articles\/choosing-the-most-suitable-mems-accelerometer-for-your-application-part-1.html."},{"key":"ref_13","unstructured":"(2022, July 25). Accelerometers. Available online: https:\/\/www.bosch-sensortec.com\/products\/motion-sensors\/accelerometers\/."},{"key":"ref_14","unstructured":"(2022, July 25). MEMS and Sensors\u2014STMicroelectronics. Available online: https:\/\/www.st.com\/en\/mems-and-sensors.html."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.measurement.2018.03.072","article-title":"Wheel Flat Detection Algorithm for Onboard Diagnostic","volume":"123","author":"Bosso","year":"2018","journal-title":"Measurement"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"107540","DOI":"10.1016\/j.ymssp.2020.107540","article-title":"An On-Board Detection Framework for Polygon Wear of Railway Wheel Based on Vibration Acceleration of Axle-Box","volume":"153","author":"Sun","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kostrzewski, M., and Melnik, R. (2021). Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review. Sensors, 21.","DOI":"10.3390\/s21144710"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Song, Y., Liang, L., Du, Y., and Sun, B. (2020). Railway Polygonized Wheel Detection Based on Numerical Time-Frequency Analysis of Axle-Box Acceleration. Appl. Sci., 10.","DOI":"10.3390\/app10051613"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mosleh, A., Meixedo, A., Ribeiro, D., Montenegro, P., and Cal\u00e7ada, R. (2022). Early Wheel Flat Detection: An Automatic Data-Driven Wavelet-Based Approach for Railways. Veh. Syst. Dyn., 1\u201330.","DOI":"10.1080\/00423114.2022.2103436"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.ymssp.2009.06.007","article-title":"Rail\u2013Wheel Interaction Monitoring Using Acoustic Emission: A Laboratory Study of Normal Rolling Signals with Natural Rail Defects","volume":"24","author":"Thakkar","year":"2010","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"107633","DOI":"10.1016\/j.apacoust.2020.107633","article-title":"Hybrid Microphone Array Signal Processing Approach for Faulty Wheel Identification and Ground Impedance Estimation in Wheel\/Rail System","volume":"172","author":"Chen","year":"2021","journal-title":"Appl. Acoust."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.1080\/15732479.2020.1832536","article-title":"An Approach for Wheel Flat Detection of Railway Train Wheels Using Envelope Spectrum Analysis","volume":"17","author":"Mosleh","year":"2021","journal-title":"Struct. Infrastruct. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1080\/09349840490443658","article-title":"Wavelet Transform for Characterizing Longitudinal and Lateral Transient Vibrations of Railroad Tracks","volume":"15","author":"Scalea","year":"2004","journal-title":"Res. Nondestruct. Eval."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.trc.2011.04.004","article-title":"Railway Wheel-Flat Detection and Measurement by Ultrasound","volume":"19","author":"Brizuela","year":"2011","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","first-page":"399","article-title":"Tram Wheel Geometry Monitoring System","volume":"Volume 1","author":"Madejski","year":"2006","journal-title":"Proceedings of the Urban Transport XII: Urban Transport and the Environment in the 21st Century"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8902","DOI":"10.1109\/TIM.2020.2998888","article-title":"Linear and Quadratic Time-Frequency Analysis of Vibration for Fault Detection and Identification of NFR Trains","volume":"69","author":"Barman","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gao, R., He, Q., Feng, Q., and Cui, J. (2020). In-Service Detection and Quantification of Railway Wheel Flat by the Reflective Optical Position Sensor. Sensors, 20.","DOI":"10.3390\/s20174969"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/1679-78255010","article-title":"Wheel-Flat Detection on Trams Using Envelope Analysis with Hilbert Transform","volume":"16","author":"Nowakowski","year":"2019","journal-title":"Lat. Am. J. Solids Struct."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1590\/1679-78256086","article-title":"Advanced Acoustic Signal Analysis Used for Wheel-Flat Detection","volume":"18","author":"Komorski","year":"2021","journal-title":"Lat. Am. J. Solids Struct."},{"key":"ref_30","unstructured":"Yue, J., Qiu, Z., and Chen, B. (2002, January 26\u201330). Application of Wavelet Transform to Defect Detection of Wheelflats of Railway Wheels. Proceedings of the 6th International Conference on Signal Processing, Beijing, China."},{"key":"ref_31","first-page":"31","article-title":"Real Time Fault Detection in Railway Tracks Using Fast Fourier Transformation and Discrete Wavelet Transformation","volume":"14","author":"Ghosh","year":"2021","journal-title":"Int. J. Inf. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.ymssp.2016.12.021","article-title":"Adaptive Parameter Blind Source Separation Technique for Wheel Condition Monitoring","volume":"90","author":"Zhang","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, X.-Z., Xu, C., and Ni, Y.-Q. (2019). Wayside Detection of Wheel Minor Defects in High-Speed Trains by a Bayesian Blind Source Separation Method. Sensors, 19.","DOI":"10.3390\/s19183981"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1016\/j.ymssp.2016.07.009","article-title":"Fault Detection Method for Railway Wheel Flat Using an Adaptive Multiscale Morphological Filter","volume":"84","author":"Li","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115963","DOI":"10.1016\/j.jsv.2021.115963","article-title":"A Two-Level Adaptive Chirp Mode Decomposition Method for the Railway Wheel Flat Detection under Variable-Speed Conditions","volume":"498","author":"Chen","year":"2021","journal-title":"J. Sound Vib."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/00423114.2019.1620956","article-title":"A Data-Driven Method for Estimating Wheel Flat Length","volume":"58","author":"Ye","year":"2020","journal-title":"Veh. Syst. Dyn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1063\/1.4823127","article-title":"Ten Lectures on Wavelets","volume":"6","author":"Daubechies","year":"1992","journal-title":"Comput. Phys."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Khani, M.E., and Arbab, M.H. (2022). Translation-Invariant Zero-Phase Wavelet Methods for Feature Extraction in Terahertz Time-Domain Spectroscopy. Sensors, 22.","DOI":"10.3390\/s22062305"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"58869","DOI":"10.1109\/ACCESS.2022.3179517","article-title":"A Review of Wavelet Analysis and Its Applications: Challenges and Opportunities","volume":"10","author":"Guo","year":"2022","journal-title":"IEEE Access"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1109\/TIE.2016.2637304","article-title":"Real-Time Power Measurement Using the Maximal Overlap Discrete Wavelet-Packet Transform","volume":"64","author":"Alves","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"822","DOI":"10.2307\/2153305","article-title":"Wavelets: Algorithms & Applications","volume":"63","author":"Lei","year":"1994","journal-title":"Math. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, D.-M. (2021). The Detection of Motor Bearing Fault with Maximal Overlap Discrete Wavelet Packet Transform and Teager Energy Adaptive Spectral Kurtosis. Sensors, 21.","DOI":"10.3390\/s21206895"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Percival, D.B., and Walden, A.T. (2000). Wavelet Methods for Time Series Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511841040"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"St\u2019ephane, M. (2009). A Wavelet Tour of Signal Processing, Elsevier.","DOI":"10.1016\/B978-0-12-374370-1.00010-0"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2243","DOI":"10.1098\/rspa.1998.0257","article-title":"The Phase\u2013Corrected Undecimated Discrete Wavelet Packet Transform and Its Application to Interpreting the Timing of Events","volume":"454","author":"Walden","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. Math. Phys. Eng. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wei, J., Liu, C., Ren, T., Liu, H., and Zhou, W. (2017). Online Condition Monitoring of a Rail Fastening System on High-Speed Railways Based on Wavelet Packet Analysis. Sensors, 17.","DOI":"10.3390\/s17020318"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lema-Condo, E.L., Bueno-Palomeque, F.L., Castro-Villalobos, S.E., Ordonez-Morales, E.F., and Serpa-Andrade, L.J. (2017, January 15\u201318). Comparison of Wavelet Transform Symlets (2-10) and Daubechies (2-10) for an Electroencephalographic Signal Analysis. Proceedings of the 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru.","DOI":"10.1109\/INTERCON.2017.8079702"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"012034","DOI":"10.1088\/1757-899X\/1045\/1\/012034","article-title":"AE Signature Analysis Using Continuous and Discrete Wavelet Transforms to Predict Grinding Wheel Conditions","volume":"1045","author":"Shivith","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:39Z","timestamp":1760141679000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,24]]},"references-count":48,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176373"],"URL":"https:\/\/doi.org\/10.3390\/s22176373","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,24]]}}}