A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

Abstract

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness (OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using (healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages: (1) data collection, (2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder (SAE), (3) data fusion based on the Mahalanobis distance, and (4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses (not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

References

[1.] MeixedoA, SantosJ, RibeiroD, et al. . Damage detection in railway bridges using traffic-induced dynamic responses. Eng Struct, 2021, 238: 112189

[2.] MeixedoA, SantosJ, RibeiroD, et al. . Online unsupervised detection of structural changes using train–induced dynamic responses. Mech Syst Signal Process, 2022, 165: 108268

[3.] CuryA, RibeiroD, UbertiniF, et al. . Structural health monitoring based on data science techniques. Structural integrity, 2021ChamSpringer

[4.] WangZ, ChaYJ. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Struct Health Monit, 2021, 20(1): 406-425

[5.] SarwarMZ, CanteroD. Deep autoencoder architecture for bridge damage assessment using responses from several vehicles. Eng Struct, 2021, 246: 113064

[6.] YessoufouF, ZhuJ. Deep autoencoder model for direct monitoring of bridges subjected to a moving vehicle load under varying temperature conditions. Structures, 2023, 52: 752-767

[7.] LeeH, LimHJ, SkinnerT, et al. . Automated fatigue damage detection and classification technique for composite structures using Lamb waves and deep autoencoder. Mech Syst Signal Process, 2022, 163: 108148

[8.] WangH, LiB, GongJ, et al. . Machine learning-based fatigue life prediction of metal materials: perspectives of physics-informed and data-driven hybrid methods. Eng Fract Mech, 2023, 284: 109242

[9.] ChaYJ, ChoiW, BüyüköztürkO. Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civil Eng, 2017, 32(5): 361-378

[10.] ChaY, ChoiW, SuhG, et al. . Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Comput Aided Civ Infrastruct Eng, 2018, 33(9): 731-747

[11.] StaśkiewiczT, FirlikB. Out-of-round tram wheels–current state and measurements. AoT, 2018, 45(1): 83-93

[12.] ChangC, CaiY, ChenB, et al. . Experimental study of the wheel/rail impact caused by wheel flat within 400 km/h using full-scale roller rig. Railw Sci, 2022, 1(1): 76-89

[13.] NielsenJCO, JohanssonA. Out-of-round railway wheels-a literature survey. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2000, 214(2): 79-91

[14.] YeY, ZhuB, HuangP, et al. . OORNet: a deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement, 2022, 199: 111268

[15.] IwnickiS, NielsenJCO, TaoG. Out-of-round railway wheels and polygonisation. Veh Syst Dyn, 2023, 61(7): 1787-1830

[16.] WuY, DuX, ZhangHJ, et al. . Experimental analysis of the mechanism of high-order polygonal wear of wheels of a high-speed train. J Zhejiang Univ Sci A, 2017, 18(8): 579-592

[17.] CaiW, ChiM, WuX, et al. . Experimental and numerical analysis of the polygonal wear of high-speed trains. Wear, 2019, 440–441: 203079

[18.] ValeC. Wheel flats in the dynamic behavior of ballasted and slab railway tracks. Appl Sci, 2021, 11(15): 7127

[19.] AminiA, EntezamiM, PapaeliasM. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Case Stud Nondestruct Test Eval, 2016, 6: 8-16

[20.] WuY, WangJ, LiuM, et al. . Polygonal wear mechanism of high-speed wheels based on full-size wheel-rail roller test rig. Wear, 2022, 494–495: 204234

[21.] YeY, WeiL, LiF, et al. . Multislice time-Frequency image entropy as a feature for railway wheel fault diagnosis. Measurement, 2023, 216: 112862

[22.] MoslehA, MontenegroPA, CostaPA, et al. . Railway vehicle wheel flat detection with multiple records using spectral kurtosis analysis. Appl Sci, 2021, 11(9): 4002

[23.] CostaC, RibeiroD, JorgeP, et al. . Calibration of the numerical model of a short-span masonry railway bridge based on experimental modal parameters. Procedia Eng, 2015, 114: 846-853

[24.] Meixedo A, Ribeiro D, Calçada R, et al (2014) Global and local dynamic effects on a railway viaduct with precast deck. In: The second international conference on railway technology: research, development and maintenance, Ajaccio, Corsica, France, 8–11 April 2014

[25.] KudvaJN, MunirN, TanPW. Damage detection in smart structures using neural networks and finite-element analyses. Smart Mater Struct, 1992, 1(2): 108-112

[26.] LieuQX. A deep neural network-assisted metamodel for damage detection of trusses using incomplete time-series acceleration. Expert Syst Appl, 2023, 233: 120967

[27.] SilvaR, GuedesA, RibeiroD, et al. . Early identification of unbalanced freight traffic loads based on wayside monitoring and artificial intelligence. Sensors, 2023, 23(3): 1544

[28.] GuedesA, SilvaR, RibeiroD, et al. . Detection of wheel polygonization based on wayside monitoring and artificial intelligence. Sensors, 2023, 23(4): 2188

[29.] MoslehA, MeixedoA, RibeiroD, et al. . Early wheel flat detection: an automatic data-driven wavelet-based approach for railways. Veh Syst Dyn, 2023, 61(6): 1644-1673

[30.] MohammadiM, MoslehA, ValeC, et al. . An unsupervised learning approach for wayside train wheel flat detection. Sensors, 2023, 23(4): 1910

[31.] GonzalezI, KaroumiR. BWIM aided damage detection in bridges using machine learning. J Civ Struct Health Monit, 2015, 5(5): 715-725

[32.] YangK, KimS, HarleyJB. Unsupervised long-term damage detection in an uncontrolled environment through optimal autoencoder. Mech Syst Signal Process, 2023, 199: 110473

[33.] LiL, MorgantiniM, BettiR. Structural damage assessment through a new generalized autoencoder with features in the quefrency domain. Mech Syst Signal Process, 2023, 184: 109713

[34.] WangZ, ChaYJ. Unsupervised machine and deep learning methods for structural damage detection: a comparative study. Eng Rep, 2022, 2022: e12551

[35.] PathirageCSN, LiJ, LiL, et al. . Structural damage identification based on autoencoder neural networks and deep learning. Eng Struct, 2018, 172: 13-28

[36.] FinottiRP, de SouzaBF, CuryAA, et al. . Numerical and experimental evaluation of structural changes using sparse auto-encoders and SVM applied to dynamic responses. Appl Sci, 2021, 11(24): 11965

[37.] YeY, HuangC, ZengJ, et al. . Shock detection of rotating machinery based on activated time-domain images and deep learning: an application to railway wheel flat detection. Mech Syst Signal Process, 2023, 186: 109856

[38.] Meng Q, Catchpoole D, Skillicom D, et al (2017) Relational autoencoder for feature extraction. In: 2017 international joint conference on neural networks (IJCNN). Anchorage, AK, USA. IEEE, pp 364–371

[39.] MoslehA, MeixedoA, RibeiroD, et al. . Automatic clustering-based approach for train wheels condition monitoring. Int J Rail Transp, 2023, 11(5): 639-664

[40.] WangP, LiC, LiangR, et al. . Fault detection and calibration for building energy system using Bayesian inference and sparse autoencoder: a case study in photovoltaic thermal heat pump system. Energy Build, 2023, 290: 113051

[41.] OlshausenBA, FieldDJ. Sparse coding with an overcomplete basis set: a strategy employed by V1?. Vis Res, 1997, 37(23): 3311-3325

[42.] MontenegroPA, CalçadaR. Wheel–rail contact model for railway vehicle–structure interaction applications: development and validation. Railw Eng Sci, 2023, 31(3): 181-206

[43.] MontenegroPA, NevesSGM, CalçadaR, et al. . Wheel–rail contact formulation for analyzing the lateral train–structure dynamic interaction. Comput Struct, 2015, 152: 200-214

[44.] HertzH. Ueber die Berührung fester elastischer Körper. Journal für die reine und angewandte Mathematik (Crelles Journal), 1882, 1882(92): 156-171

[45.] KalkerJJ. Book of tables for the Herzian creep-force law, 1996DelftFaculty of Technical Mathematics and Informatics, Delft University of Technology

[46.] The MathWorks Inc (2018) MATLAB^®, R2018a. Natick, Massachusetts

[47.] Ansys Inc (2018) ANSYS^®, Release 19.2. Academic Research, Canonsburg P

[48.] RibeiroD, CalçadaR, BrehmM, et al. . Calibration of the numerical model of a track section over a railway bridge based on dynamic tests. Structures, 2021, 34: 4124-4141

[49.] RibeiroD, CalçadaR, DelgadoR, et al. . Finite-element model calibration of a railway vehicle based on experimental modal parameters. Veh Syst Dyn, 2013, 51(6): 821-856

[50.] MoslehA, MontenegroP, Alves CostaP, et al. . An approach for wheel flat detection of railway train wheels using envelope spectrum analysis. Struct Infrastruct Eng, 2021, 17(12): 1710-1729

[51.] Vale C, Calçada R (2010) Dynamic response of a coupled vehicle–track system to real longitudinal rail profiles. In: Proceedings of the tenth international conference on computational structures technology, Valencia, Spain, 14–17 Sept. 2010

[52.] European Committee for Standardization (2006) Railway applications—track—track geometry quality—part 2: measuring systems—track recording vehicles (EN 13848-2)

[53.] MoslehA, CostaPA, CalçadaR. A new strategy to estimate static loads for the dynamic weighing in motion of railway vehicles. Proc Inst Mech Eng Part F J Rail Rapid Transit, 2020, 234(2): 183-200

[54.] CasasJR, MoughtyJJ. Bridge damage detection based on vibration data: past and new developments. Front Built Environ, 2017, 3: 4

[55.] MuJ, ZengJ, HuangC, et al. . Experimental and numerical investigation into development mechanism of wheel polygonalization. Eng Fail Anal, 2022, 136: 106152

[56.] TaoG, XieC, WangH, et al. . An investigation into the mechanism of high-order polygonal wear of metro train wheels and its mitigation measures. Veh Syst Dyn, 2021, 59(10): 1557-1572

[57.] ZhangJ, HanGX, XiaoXB, et al. . Influence of wheel polygonal wear on interior noise of high-speed trains. J Zhejiang Univ Sci, 2018, A15: 1002-1018

[58.] Peng B (2020) Mechanisms of railway wheel polygonization. Dissertaion, University of Huddersfield

[59.] HuH, LiuJ, ZhangX, et al. . An effective and adaptable K-means algorithm for big data cluster analysis. Pattern Recognit, 2023, 139: 109404

[60.] Andrade NunesL, Piazzaroli Finotti AmaralR, de SouzaBF, et al. . A hybrid learning strategy for structural damage detection. Struct Health Monit, 2021, 20(4): 2143-2160

[61.] AlvesV, CuryA, CremonaC. On the use of symbolic vibration data for robust structural health monitoring. Proc Inst Civ Eng Struct Build, 2016, 169(9): 715-723

[62.] BarileC, CasavolaC, PappaletteraG, et al. . Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique. Appl Acoust, 2022, 185: 108425

[63.] MeixedoA, RibeiroD, SantosJ, et al. . Real-time unsupervised detection of early damage in railway bridges using traffic-induced responses. Structural health monitoring based on data science techniques, 2022ChamSpringer117-142

[64.] CalinskiT, HarabaszJ. A dendrite method for cluster analysis. Commun Stat Theory Meth, 1974, 3(1): 780133830

[65.] Solorio-FernándezS, Carrasco-OchoaJA, Martínez-TrinidadJF. A new hybrid filter–wrapper feature selection method for clustering based on ranking. Neurocomputing, 2016, 214: 866-880

Funding

Agência Nacional de Inova??o(NORTE-01-0247-FEDER-069595); Funda??o para a Ciência e a Tecnologia(04708/2020)