Failures in wheelsets and bearings are a leading cause of train derailments and reduced system availability. Condition monitoring allows detection of developing faults and estimation of the remaining useful life of components. Recent advances in computer-based fault detection have improved the diagnosis of railway assets such as wheelsets, axles, bearings, and tracks. Industry 4.0 technologies, including big data analytics, cybersecurity, and the Internet of Things, have enhanced detection accuracy, efficiency, and real-time decision-making. However, since Industry 4.0 is so focused on technology, it often ignores things that are important to people. The new idea of Industry 5.0 combines human knowledge with intelligent systems to make maintenance plans that are long-lasting and flexible. This study evaluates the efficacy of railway component defect detection and diagnostic methodologies within the framework of Industry 4.0, and it delves into the possibilities and threats associated with the shift to Industry 5.0 for future railway maintenance systems.
GandhiAPPadhiBK. Disaster response, confined space medicine and safety systems against railway accidents: 2023 Odisha railway accident from India. Public Health Challenges2023; 2: 2–5.
4.
CaoYAnYSuS, et al.A statistical study of railway safety in China and Japan 1990–2020. Accid Anal Prev2022; 175: 106764.
5.
AndersenT. Analysis of past derailments: information from data bases, investigation reports and surveys. DNV presentation on the results of the D-RAIL FP7 Project.
6.
PapaeliasMHuangZAminiA, et al.Advanced wayside condition monitoring of rolling stock wheelsets. 11th European conference on non-destructive testing (ECNDT 2014).
7.
ChenBYanZChenW. Defect detection for wheel-bearings with time-spectral kurtosis and entropy. Entropy2014; 16: 607–626.
8.
KunduPDarpeASinghSP, et al.A review on condition monitoring technologies for railway rolling stock. Fourth European Conference of the Prognostics and Health Management Society2018; 4: 1–15.
9.
SaniukSGrabowskaS. Concept and its impact on achieving a higher level of sustainable consumption.
10.
ParidaVSjödinDReimW. Reviewing literature on digitalization, business model innovation, and sustainable industry: past achievements and future promises. Sustainability2019; 11: 391. Epub ahead of print 2019.
11.
CiffolilliAMuscioA. Industry 4.0: national and regional comparative advantages in key enabling technologies. Eur Plan Stud2018; 26: 2323–2343.
12.
IainMCHendersonRSternS. The impact of artificial intelligence on innovation: an exploratory analysis. 2018.
13.
Sondermann-wC. Integration of condition monitoring in self-optimizing function modules applied to the active railway guidance module. IEEE, 2010, vol 3, pp. 65–74.
14.
JardineAKSLinDBanjevicD. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process2006; 20: 1483–1510.
15.
KänsäläKRantalaSKauppilaO, et al.Acceleration sensor technology for rail track asset condition monitoring. Proc Inst Civ Eng: Management, Procurement and Law2018; 171: 32–40.
16.
WestonPFRobertsCGoodmanCJ, et al.Condition monitoring of railway track using in-service trains. IET Seminar Digest2006; 2006: 26–31.
17.
LeeJSChoiSKimSS, et al.A mixed filtering approach for track condition monitoring using accelerometers on the axle box and bogie. IEEE Trans Instrum Meas2012; 61: 749–758.
18.
ChellaswamyCAkilaVDinesh BabuA, et al.Fuzzy logic based railway track condition monitoring system. In: 2013 IEEE international conference on emerging trends in computing, communication and nanotechnology, ICE-CCN 2013, 2013, pp. 250–255.
19.
BagshaweMJ. Investigating inertial measurements using MEMS devices in trainborne automatic track condition monitoring applications. In: IET conference on control and automation 2013: uniting problems and solutions, 2013, pp. 1–6.
20.
QinLWeiDSunYB. Design of onboard device to diagnose track fault online. Appl Mech Mater2014; 483: 465–470.
21.
WardCPGoodallRMDixonR, et al.Condition monitoring of rail vehicle bogies. In: IET Seminar Digest, 2010, pp. 1178–1183.
22.
WardCPGoodallRMDixonR. Contact force estimation in the railway vehicle wheel-rail interface. In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2011, pp. 4398–4403.
23.
MatsumotoASatoYOhnoH, et al.Continuous observation of wheel/rail contact forces in curved track and theoretical considerations. Veh Syst Dyn2012; 50: 349–364.
24.
XuPYaoWZhaoY, et al.Condition monitoring of wheel wear for high-speed trains: a data-driven approach. In: 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018. Epub ahead of print 2018.
25.
BarmanJKHazarikaD. Condition monitoring of NFR trains with measurements from a single wayside 3D vibration sensor. IEEE Sens J2020; 20: 4096–4103.
26.
LiCLuoSColeC, et al.An overview: modern techniques for railway vehicle on-board health monitoring systems. Veh Syst Dyn2017; 55: 1045–1070.
27.
LiCLuoSColeC, et al.Bolster spring fault detection strategy for heavy haul wagons. Veh Syst Dyn2018; 56: 1604–1621.
28.
GomezMJCastejonCCorralE, et al.Railway axle early fatigue crack detection through condition monitoring techniques. Sensors2023; 23: 6143. Epub ahead of print 1 July 2023.
29.
GómezMJCastejónCCorralE, et al.Railway axle condition monitoring technique based on wavelet packet transform features and support vector machines. Sensors2020; 20: 1–18.
30.
López GaldoAGuerrero-LópezAOlmosPM, et al.Detecting train driveshaft damages using accelerometer signals and differential convolutional neural networks. Eng Appl Artif Intell2023; 126: 106840. Epub ahead of print 1 November 2023.
31.
HassanMBruniSCarboniM. Crack detection in railway axle using horizontal and vertical vibration measurements. 2016.
32.
RolekPBruniSCarboniM. Condition monitoring of railway axles based on low frequency vibrations. Int J Fatigue2016; 86: 88–97.
33.
ZhengZSongDXuX, et al.A fault diagnosis method of bogie axle box bearing based on spectrum whitening demodulation. Sensors2020; 20: 1–19.
34.
SymondsNCorniIWoodRJK, et al.Observing early stage rail axle bearing damage. Eng Fail Anal2015; 56: 216–232.
35.
LiuCZhangXWangR, et al.Vibration-based detection of axlebox bearing considering inner and outer ring raceway defects. Lubricants2024; 12: 142, Epub ahead of print 1 May 2024.
36.
PennacchiPChattertonSVaniaA, et al.Definition of damage indices for railway axle bearings: results of long-lasting tests. Machines2021; 9: 1–17.
37.
LiuZDingJLinJ, et al.A rolling bearing fault diagnosis-optimized scale-space representation for the empirical wavelet transform. Shock Vib2018; 2018: 2749689, Epub ahead of print 2018.
38.
YiCWangDFanW, et al.Eemd-based steady-state indexes and their applications to condition monitoring and fault diagnosis of railway axle bearings. Sensors2018; 18: 704, Epub ahead of print 1 March 2018.
39.
ChenBZhangWXi GuJ, et al.Product envelope spectrum optimization-gram: an enhanced envelope analysis for rolling bearing fault diagnosis. Mech Syst Signal Process2023; 193: 110270, Epub ahead of print 15 June 2023.
40.
MilovancevicMDenićNPetrovićM, et al.Evaluation of vibration spectrums of railway vehicles. Journal of Vibration Engineering and Technologies2024; 12: 129–138.
41.
ZhaojinY. Wavelet resonance demodulation method and its application in fault diagnosis of railway bearings. In: Journal of Physics: Conference Series. Institute of Physics Publishing, 2020. Epub ahead of print 17 April 2020.
42.
WangKHuangYZhangB, et al.Improved synchronous sampling and its application in high-speed railway bearing damage detection. Machines2024; 12; 101. Epub ahead of print 1.
43.
MannanADanielWJTMeehanPA. The investigation of the contact and wear of railway axle bearings under various race conformities. Wear2023; 532–533: 205117, Epub ahead of print 15 November 2023.
44.
YuanFQLuJM. Improved condition monitoring system to protect railway axle bearing safety from electric corrosion. In: 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)IEEE, 2015, pp. 1634–1638.
45.
EntezamiMRobertsCWestonP, et al.Perspectives on railway axle bearing condition monitoring. Proc Inst Mech Eng F J Rail Rapid Transit2020; 234: 17–31.
46.
BernalESpiryaginMColeC. Onboard condition monitoring sensors, systems and techniques for freight railway vehicles: a review. IEEE Sens J2019; 19: 4–24.
47.
SomàAAimarMZanardelliA. Experimental data measured with an on Board Unit for condition monitoring of freight wagons [Dati sperimentali misurati con On Board Unit per il monitoraggio delle condizioni dei carri merci]. Ing Ferrov2017; 72: 547–567.
48.
WolfMRudolphMKanounO. Concept for an event-triggered wireless sensor network for vibration-based diagnosis in trams. Vibroengineering Procedia2019; 27: 55–60.
49.
SabbioniECarboniMTarsitanoD, et al.Crack detection in railway axles using continuous monitoring and periodic non-destructive inspection: experimental investigation on a freight wagon using a full-scale roller rig. Struct Health Monit2024; 24: 3487–3502, Epub ahead of print.
50.
MontalvoJTarawnehCFuentesAA. Vibration-based defect detection for freight railcar tapered-roller bearings. In: ASME/IEEE Joint Rail Conference. American Society of Mechanical Engineers, 2018, p. V001T06A011.
51.
PamsJTarawnehCRochaD, et al.Optimized vibration-based health metrics for freight rail bearings. In: ASME/IEEE Joint Rail Conference. American Society of Mechanical Engineers, 2024, p. V001T05A010.
52.
ChellaswamyCAishwaryaRMurthySLN, et al.Green controller for rail track condition monitoring and information system. In: Proceedings of the 2013 international conference on green computing, communication and conservation of energy, ICGCE 2013, 2013, pp. 562–567.
53.
WangLUnsiwilaiSZengY, et al.Condition monitoring of railway transition zones using acceleration measurements on multiple axle boxes: case studies in the Netherlands, Sweden, and Norway. In: Lecture Notes in Mobility. Springer, 2026, pp. 290–296.
54.
GhiasiRMalekjafarianA. Monitoring of railway tracks maintenance needs using dynamic responses collected by an in-service train. Railway Engineering Science2025; 33: 553–580.
55.
ShaikhMZMehranSBaroEN, et al.Design and development of a wayside AI‐Assisted vision system for online train wheel inspection. Eng Rep2025; 7: e13027.
56.
HassanIUPanduruKWalshJ. An In-Depth study of vibration sensors for condition monitoring. Sensors2024; 24: 740, Epub ahead of print 1 February 2024.
57.
MaQYangSLiuY, et al.Dynamic analysis of axle box bearings on the high-speed train caused by wheel-rail excitation. Appl Math Mech2024; 45: 441–460.
58.
CelińskiIBurdzikRMłyńczakJ, et al.Research on the applicability of vibration signals for real‐time train and track condition monitoring. Sensors2022; 22: 2368, Epub ahead of print 1 March 2022.
ZamoranoMGómezMJCastejonC, et al.Analysis of acoustic emission waveforms by wavelet packet transform for the detection of crack initiation due to fretting fatigue in solid railway axles. Appl Sci2025; 15: 8435.
61.
CarboniMCrivelliD. An acoustic emission based structural health monitoring approach to damage development in solid railway axles. Int J Fatigue2020; 139: 105753, Epub ahead of print 1 October 2020.
62.
CarboniMZamoranoM. On monitoring fretting fatigue damage in solid railway axles by acoustic emission with unsupervised machine learning and comparison to non-destructive testing techniques. Proc Inst Mech Eng F J Rail Rapid Transit2024; 238: 256–267.
63.
LinLZhouKLiD, et al.Experimental study on train axle fatigue crack acoustic emission signals recognition based on a one-dimensional convolutional neural network. Nondestr Test Eval2025; 40: 2950–2981.
64.
AminiAHuangZEntezamiM, et al.Evaluation of the effect of speed and defect size on high-frequency acoustic emission and vibration condition monitoring of railway axle bearings. Insight-Non-Destructive Testing and Condition Monitoring2017; 59: 184–188.
65.
PapaeliasMAminiAHuangZ, et al.Online condition monitoring of rolling stock wheels and axle bearings. Proc Inst Mech Eng F J Rail Rapid Transit2016; 230: 709–723.
66.
YanGChenJBaiY, et al.A survey on fault diagnosis approaches for rolling bearings of railway vehicles. Processes2022; 10: 724, Epub ahead of print 1 April 2022.
67.
EntezamiMStewartETutcherJ, et al.Acoustic analysis techniques for condition monitoring of roller bearings. In: 6th IET Conference on Railway Condition Monitoring (RCM 2014). IET, 2014, pp. 1–5.
68.
WangSMeiGChenB, et al.Fault diagnosis of rolling bearings based on enhanced optimal morphological gradient product filtering. Measurement2022; 196: 166–184, Epub ahead of print 15.
69.
AravanisTCISakellariouJSFassoisSD. Spectral analysis of railway vehicle vertical vibration under normal operating conditions. Int J Real Ther2016; 4: 193–207.
70.
AminiAEntezamiMPapaeliasM. Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals. Case Studies in Nondestructive Testing and Evaluation2016; 6: 8–16.
71.
GongYWangSXieZ, et al.Self-powered wireless sensor node for smart railway axle box bearing via a variable reluctance energy harvesting system. IEEE Trans Instrum Meas2021; 70: 1–11, Epub ahead of print.
72.
SánchezRVLuceroPMacancelaJC, et al.Evaluation of time and frequency condition indicators from vibration signals for crack detection in railway axles. Appl Sci2020; 10: 1–19.
73.
VanrajDSSPablaBS. Gear fault classification using vibration and acoustic sensor fusion: a case study. In: 2018 Condition Monitoring and Diagnosis, CMD 2018 - Proceedings, 2018, vol 0–5.
74.
ChandranPRantataloMOdeliusJ, et al.Train-based differential eddy current sensor system for rail fastener detection. Meas Sci Technol2019; 30: 125105, Epub ahead of print 2019.
75.
OlivierBGuoFQianY, et al.A review of computer vision for railways. IEEE Trans Intell Transport Syst2025; 26: 11034–11065.
76.
KarakoseMYamanOMuratK, et al.A new approach for condition monitoring and detection of rail components and rail track in railway. Int J Comput Intell Syst2018; 11: 830–845.
77.
ZhangYLiuFJingY, et al.Application of FBG sensing technique for monitoring and early warning system of high-speed railway track conditions. 25th International Conference on Optical Fiber Sensors2017; 10323: 103237X.
78.
LiuYWHsiehHYChenN. High-speed frame grabber for track monitoring systems. In: 2011 joint rail conference, JRC 2011, 2011, pp. 159–164.
79.
AlemiACormanFLodewijksG. Condition monitoring approaches for the detection of railway wheel defects. Proc Inst Mech Eng F J Rail Rapid Transit2017; 231: 961–981.
80.
NielsenJCOJohanssonA. Out-of-round railway wheels-a literature survey. Proc Inst Mech Eng F J Rail Rapid Transit2000; 214: 79–91.
81.
AttivissimoFDaneseAGiaquintoN, et al.A railway measurement system to evaluate the wheel-rail interaction quality. IEEE Trans Instrum Meas2007; 56: 1583–1589.
82.
JohanssonANielsenJCO. Johansson, Nielsen - unknown - out-of-round railway wheelsÐwheel ± rail contact forces and track response derived from ® eld tests and.pdf. Journal of Rail and Rapid Transit2003; 217: 135–146.
83.
MoriHTsunashimaHKojimaT, et al.Condition monitoring of railway track using In-service vehicle. Journal of Mechanical Systems for Transportation and Logistics2010; 3: 154–165.
84.
AsplundMLinJRantataloM. Assessment of the data quality of wayside wheel profile measurements. Int J COMADEM2016; 19: 19–25.
85.
LeiLSongDLiuZ, et al.Displacement identification by computer vision for condition monitoring of rail vehicle bearings. Sensors2021; 21: 1–19.
86.
TamHYLeeKKLiuSY, et al.Intelligent optical fibre sensing networks facilitate shift to predictive maintenance in railway systems. In: 2018 international conference on intelligent rail transportation, ICIRT 2018, 2019, pp. 2–5.
87.
MoslehAMeixedoARibeiroD, et al.Early wheel flat detection: an automatic data-driven wavelet-based approach for railways. Veh Syst Dyn2023; 61: 1644–1673.
88.
YellaSDoughertyMSGuptaNK. Artificial intelligence techniques for the automatic interpretation of data from non-destructive testing. Insight: Non-Destructive Testing and Condition Monitoring2006; 48: 10–20.
89.
FinkOWangQSvensénM, et al.Potential, challenges and future directions for deep learning in prognostics and health management applications. Eng Appl Artif Intell2020; 92: 103678.
90.
NunezAHendriksJLiZ, et al.Facilitating maintenance decisions on the Dutch railways using big data: the ABA case study. In: Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, 2015, vol 73, pp. 48–53.
91.
UnalMOnatMDemetgulM, et al.Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network. Measurement2014; 58: 187–196.
92.
SuZNúñezAJamshidiA, et al.Model predictive control for maintenance operations planning of railway infrastructures. Lect Notes Comput Sci2015; 9335: 673–688.
93.
AhmadianMAnnisNDTempletonB, et al.A preliminary evaluation and analysis of technologies for eliminating railway insulated joints. American Society of Mechanical Engineers, Rail Transportation Division (Publication) RTD, 2005, vol 30, pp. 135–142.
94.
FumeoEOnetoLAnguitaD. Condition based maintenance in railway transportation systems based on big data streaming analysis. In: Procedia Computer Science. Elsevier B.V., 2015, pp. 437–446.
95.
LiuLSongDGengZ, et al.A real-time fault early warning method for a high-speed EMU axle box bearing. Sensors2020; 20: 823, Epub ahead of print 1 February 2020.
96.
LuoHBoLPengC, et al.Fault diagnosis for high‐speed train axle‐box bearing using simplified shallow information fusion convolutional neural network. Sensors2020; 20: 1–23.
97.
CrespoMAde la Fuente CarmonaAMarcosJA, et al.Designing CBM plans, based on predictive analytics and big data tools, for train wheel bearings. Comput Ind2020; 122: 103292, Epub ahead of print 1 November 2020.
98.
WilsonBMDickMG. Development of a predictive life tool for tapered roller bearings using measured residual stress and retained austenite data. Powder Diffr2008; 23: 118–120.
99.
ChengYChenBZhangW. Adaptive multipoint optimal minimum entropy deconvolution adjusted and application to fault diagnosis of rolling element bearings. IEEE Sens J2019; 19: 12153–12164.
100.
LiYLiangXChenY, et al.Wheelset bearing fault detection using morphological signal and image analysis. Struct Control Health Monit2020; 27: e2619, Epub ahead of print 1 November 2020.
101.
XinGLiZJiaL, et al.Fault diagnosis of wheelset bearings in high-speed trains using logarithmic short-time fourier transform and modified self-calibrated residual network. IEEE Trans Ind Inf2022; 18: 7285–7295.
102.
PengDLiuZWangH, et al.A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access2019; 7: 10278–12093.
103.
GuoWStevenLQiangM. 2019 prognostics and system health management conference: PHM-Qingdao: october 25-27, 2019, Qingdao, China. IEEE, 2019.
104.
YaoJGuX. A subdomain adaptation based bearing fault diagnosis method using infrared thermal images. In: Proceedings of 2024 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2024. Institute of Electrical and Electronics Engineers Inc., 2024, pp. 271–277.
105.
XinGQinYJiaL-M, et al.Low-rank and sparse model: a new perspective for rolling element bearing diagnosis. In: 2018 International Conference on Intelligent Rail Transportation (ICIRT). IEEE, 2018, pp. 1–5.
106.
KreuzerMSchmidtAKellermannW. Novel features for the detection of bearing faults in railway vehicles. In: INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering, 2021, pp. 3833–3844.
107.
BöhmTDoegenC. Diagnosis without sensors: integration of external data for condition monitoring of railway switches. In: Proceedings of the 23rd International Congress on Condition Monitoring and Diagnostic Engineering Management. Sunrise Publishing Limitied, 2010, pp. 619–622.
108.
HenaoHKiaSHCapolinoGA. Torsional-vibration assessment and gear-fault diagnosis in railway traction system. IEEE Trans Ind Electron2011; 58: 1707–1717.
109.
RenfrewAC. Condition monitoring of railway electric point machines. IEE Proc Elec Power Appl2002; 149(8): 465–473.
110.
OyebandeBORenfrewAC. The design and implementation of a simulated trackside environment test system for an electric point machine. Proc Inst Mech Eng F J Rail Rapid Transit2007; 221: 477–485.
111.
ZhouFArcherNBowlesJ, et al.Remote condition monitoring and validation of railway points. Computing and Control Engineering Journal2002; 13: 221–230.
112.
ChenJRobertsCWestonP. Neuro-fuzzy fault detection and diagnosis for railway track circuits. IFAC Proc Vol2006; 39: 1366–1371.
113.
ZhangJHeZY. Distribution system fault diagnosis scheme based on multiple information sources: for railway passenger dedicated line. In: Asia-Pacific Power and Energy Engineering Conference. APPEEC, 2009. Epub ahead of print 2009.
114.
TastimurCYamanOKarakoseM, et al.A real time interface for vision inspection of rail components and surface in railways. In: IDAP 2017 - International Artificial Intelligence and Data Processing Symposium, 2017. Epub ahead of print 2017.
115.
YamanOKarakoseMAkinE. A vision based diagnosis approach for multi rail surface faults using fuzzy classificiation in railways. In: 2nd International Conference on Computer Science and Engineering. UBMK 2017, 2017, pp. 713–718.
116.
QiSChunjunCYanpingL, et al.Extraction of harmonic signatures from broadband noise and its application in the fault diagnosis of wheel polygonization. In: 25th International Congress on Sound and Vibration 2018, ICSV 2018: Hiroshima Calling, 2018, pp. 4047–4053.
117.
LiYZhangWXiongQ, et al.A novel fault diagnosis model for bearing of railway vehicles using vibration signals based on symmetric alpha-stable distribution feature extraction. Shock Vib2016; 2016: 1–13. Epub ahead of print 2016.
118.
YaoDYangJPangZ, et al.Railway axle box bearing fault identification using LCD-MPE and ELM-Adaboost. Journal of Vibroengineering2018; 20: 165–174.
119.
ZhuZWangHLiuZ, et al.Fault diagnosis of wheelset bearings using deep bidirectional long short-term memory network. In: 2019 Prognostics and System Health Management Conference, PHM-Qingdao 2019, 2019. Epub ahead of print 2019. DOI: 10.1109/PHM-Qingdao46334.2019.8942870.
120.
ShimJKimGChoB, et al.Application of vibration signal processing methods to detect and diagnose wheel flats in railway vehicles. Appl Sci2021; 11: 1–18.
121.
WeiXJiaLGuoK, et al.Fault isolation for urban railway vehicle suspension systems. In: IFAC Proceedings Volumes (IFAC-PapersOnline), 2014, pp. 12122–12127.
122.
LewisRWMaddisonSStewartEJC. An extensible framework architecture for wireless condition monitoring applications for railway rolling stock. In: IET Conference Publications, 2014. Epub ahead of print 2014.
123.
GuXMaoZJiangB, et al.Multiple-model based fault diagnosis for actuators of rail vehicle suspension. In: Proceedings of the 2015 27th Chinese Control and Decision Conference. CCDC 2015, 2015, pp. 1676–1681.
124.
JungHMünkerTKampmannG, et al.A probabilistic approach for fault detection of railway suspensions. In: Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance - Proceedings of the 11th International Workshop on Structural Health Monitoring. IWSHM 2017, 2017, pp. 2765–2772.
125.
YeYZhangYWangQ, et al.Fault diagnosis of high-speed train suspension systems using multiscale permutation entropy and linear local tangent space alignment. Mech Syst Signal Process2020; 138: 106565, Epub ahead of print 2020.
126.
XuXYaoH. Application of optimized BP network in bogie condition monitoring. In: Resilience and Sustainable Transportation Systems - Selected Papers from the 13th Asia Pacific Transportation Development Conference, 2020, pp. 157–165.
127.
YeYZhuBHuangP, et al.OORNet: a deep learning model for on-board condition monitoring and fault diagnosis of out-of-round wheels of high-speed trains. Measurement2022; 199: 111268, Epub ahead of print 2022.
128.
TrillaAMirallesDFernándezV. Pushing distributed vibration analysis to the edge with a low-resolution companding autoencoder: industrial iot for phm. In: Annual Conference of the PHM Society, 2020, p. 9.
129.
GuanTDengXWangJ. Dynamic response of axle box bearing for high-speed train considering wheelset flexibility and polygonal wear. Sci Rep2023; 13: 22680, Epub ahead of print 1 December 2023.
130.
LiYLiangXLinJ, et al.Train axle bearing fault detection using a feature selection scheme based multi-scale morphological filter. Mech Syst Signal Process2018; 101: 435–448.
131.
FallahnezhadKLiuSBrinjiO, et al.Monitoring and modelling of false brinelling for railway bearings. Wear2019; 424–425: 151–164.
132.
WangDZhaoXKouLL, et al.A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis. Mech Syst Signal Process2019; 122: 754–768.
133.
ChenBChengYCaoH, et al.Generalized statistical indicators-guided signal blind deconvolution for fault diagnosis of railway vehicle axle-box bearings. IEEE Trans Veh Technol2025; 74: 2458–2469.
134.
ChenBChengYZhangW, et al.Investigation on enhanced mathematical morphological operators for bearing fault feature extraction. ISA Trans2022; 126: 440–459.
135.
XiongQZhangWXuY, et al.Alpha-stable distribution and multifractal detrended fluctuation analysis-based fault diagnosis method application for axle box bearings. Shock Vib2018; 2018: 1737219, Epub ahead of print 2018.
136.
LiYFengKChenY, et al.Multioperator morphological undecimated wavelet for wheelset bearing compound fault detection. IEEE Trans Instrum Meas2023; 72: 1-12, Epub ahead of print 2023.
137.
WuJLiYJiaL, et al.Semi-supervised fault diagnosis of wheelset bearings in high-speed trains using autocorrelation and improved flow Gaussian mixture model. Eng Appl Artif Intell2024; 132: 107861, Epub ahead of print 1.
138.
YaoDYangJLiX, et al.A hybrid approach for fault diagnosis of railway rolling bearings using STWD-EMD-GA-LSSVM. Math Probl Eng2016; 2016: 8702970, Epub ahead of print 2016.
139.
Tobon-MejiaDADersinPTripotG. Challenges and opportunities in applying vibration based condition monitoring in railways. In: Annual Conference of the PHM Society, 2017.
140.
MontalvoJTarawnehCLimaJ, et al.Estimating the outer ring defect size and remaining service life of freight railcar bearings using vibration signatures. In: ASME/IEEE Joint Rail Conference. American Society of Mechanical Engineers, 2019, p. V001T06A006.
141.
CuanangJTarawnehCAmaroM, et al.Optimization of railroad bearing health monitoring system for wireless utilization. In: 2020 Joint Rail Conference, JRC 2020. American Society of Mechanical Engineers (ASME), 2020. Epub ahead of print 2020.
142.
MourtzisD. Challenges and future perspectives for the life cycle of manufacturing networks in the mass customisation era. Logistics Research2016; 9: 1–20.
143.
ElMaraghyHMonostoriLSchuhG, et al.Evolution and future of manufacturing systems. CIRP Ann2021; 70: 635–658.
144.
KagermannHLukasWWahlsterW. Industry 4.0: with the internet of things on the way to the 4th industrial revolution. VDI news2011; 13: 2–3.
OztemelEGursevS. Literature review of industry 4.0 and related technologies. J Intell Manuf2020; 31: 127–182.
147.
LongoFNicolettiLPadovanoA. Ubiquitous knowledge empowers the smart factory: the impacts of a service-oriented Digital Twin on enterprises’ performance. Annu Rev Control2019; 47: 221–236.
148.
LongoFNicolettiLPadovanoA. Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Comput Ind Eng2017; 113: 144–159.
149.
NahavandiS. Industry 5. 0. Sustainability2019; 11: 43–71.
150.
SawHSbin AzmiAAChewKW, et al.4.2 sustainability and development of industry 5.0. In: The Prospect of Industry 50 in Biomanufacturing, 2021, vol 287.
151.
SindhwaniRAfridiSKumarA, et al.Can industry 5.0 revolutionize the wave of resilience and social value creation? A multi-criteria framework to analyze enablers. Technol Soc2022; 68: 101887.
152.
JavaidMHaleemA. Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. Journal of Industrial Integration and Management2020; 5: 327–348.
153.
MaddikuntaPKRPhamQVBP, et al.Industry 5.0: a survey on enabling technologies and potential applications. J Ind Inf Integr2022; 26: 100257.
154.
JavaidMHaleemASinghRP, et al.Industry 5.0: potential applications in COVID-19. J Ind Integ Manag2020; 05: 507–530.
155.
AkundiAEurestiDLunaS, et al.State of industry 5.0—Analysis and identification of current research trends. Applied System Innovation2022; 5: 1–14.
156.
VogtJ. Where is the human got to go? Artificial intelligence, machine learning, big data, digitalisation, and human–robot interaction in industry 4.0 and 5.0. AI Soc2021; 36: 1083–1087.
157.
RomeroDBernusPNoranO, et al.The operator 4.0: human cyber-physical systems & adaptive automation towards human-automation symbiosis work systems. In: Advances in production management systems initiatives for a sustainable world IFIP AICT, 2017, vol. 488. https://www.springer.com/series/6102
158.
RomeroDStahreJ. Towards the resilient operator 5.0: the future of work in smart resilient manufacturing systems. Proced CIRP2021; 104: 1089–1094.
159.
ChourasiaSTyagiAPandeySM, et al.Sustainability of industry 6.0 in global perspective: benefits and challenges. MAPAN-J Metrol Soc India2022; 37: 443–452.
160.
RüßmannMLorenzMGerbertP, et al.Industry 4. 0: the future of productivity and growth in manufacturing industries.
161.
XuXLuYVogel-heuserB, et al.Industry 4. 0 and industry 5. 0 — inception, conception and perception. J Manuf Syst2021; 61: 530–535.
162.
ZizicMCMladineoMGjeldumN, et al.From industry 4.0 towards industry 5.0: a review and analysis of paradigm shift for the people, organization and technology. Energies2022; 15: 5221, Epub ahead of print 2022.
163.
LeeY-J. Human-centered intelligent systems for railroad energy management: a behavioral analysis framework using machine learning for technology assessment and social impact. HumaCent Intell Sys2025; 5351–375, Epub ahead of print 25.
164.
MüllerJ. Enabling technologies for results of a workshop with Europe ’ s technology leaders. Epub ahead of print 2020.
165.
FernandesTMagalhãesJPAlvesW. Cybersecurity in smart railways: exploring risks, vulnerabilities and mitigation in the data communication services. Green Energy Intell Transp2025; 4: 100305, Epub ahead of print 1 August 2025.
166.
KourRKarimRThaduriA. Cybersecurity for railways–A maturity model. Proc Inst Mech Eng F J Rail Rapid Transit2020; 234: 1129–1148.
167.
PredescuSGSavuDBadeaVE. Cybersecurity in the railway sector. Romanian Cyber Security Journal2022; 4: 95–104.
168.
GbadamosiAQOyedeleLODelgadoJMD, et al.IoT for predictive assets monitoring and maintenance: an implementation strategy for the UK rail industry. Autom Constr2021; 122: , Epub ahead of print 1.
169.
ZefrehMMHussainBSiposT. In-Depth analysis and model development of passenger satisfaction with public transportation. KSCE J Civ Eng2020; 24: 3064–3073.
170.
Islam. Measuring customer’s satisfaction on bus transportation. Am J Econ Bus Adm2014; 6: 34–41.
171.
AmponsahCTAdamsS. Service quality and customer satisfaction in public transport operations. Int J Serv Oper Manag2016; 25: 531–549.
172.
GjermëniOPjeroE. Statistical analyses of impressions related to the customer’s experience while attending restaurants located in Vlore, Albania. Sci Technol Aliment2020; 25: 46–61.
173.
AbenozaRFCatsOSusiloYO. Travel satisfaction with public transport: determinants, user classes, regional disparities and their evolution. Transp Res Part A Policy Pract2017; 95: 64–84.
174.
GuiraoBGarcía-PastorALópez-LambasME. The importance of service quality attributes in public transportation: narrowing the gap between scientific research and practitioners’ needs. Transp Policy2016; 49: 68–77.
175.
TyrinopoulosYAntoniouC. Public transit user satisfaction: variability and policy implications. Transp Policy2008; 15: 260–272.
176.
BörjessonMRubenssonI. Satisfaction with crowding and other attributes in public transport. Transp Policy2019; 79: 213–222.
177.
CantwellMCaulfieldBO’MahonyM. Examining the factors that impact public transport commuting satisfaction. J Public Trans2009; 12: 1–21.
178.
NathanailE. Measuring the quality of service for passengers on the hellenic railways. Transp Res Part A Policy Pract2008; 42: 48–66.
179.
ChengY. Exploring passenger anxiety associated with train travel, 2010, pp. 875–896.
180.
YangJShiwakotiN. Passengers ’ perception of satisfaction and its relationship with travel experience attributes: results from an Australian survey.
181.
NaweedARainbirdS. Recovering time or chasing rainbows? Exploring time perception, conceptualization of time recovery, and time pressure mitigation in train driving. IIE Trans Occup2015; 3: 91–104.
182.
PhillipsROSagbergF. Helping train drivers pass signals safely: lessons from ten case studies. 2010.
183.
FiltnessAJNaweedA. Causes, consequences and countermeasures to driver fatigue in the rail industry: the train driver perspective. Appl Ergon2017; 60: 12–21.
184.
NaweedABalakrishnanGDorrianJ. Going solo: hierarchical task analysis of the second driver in “two-up” (multi-person) freight rail operations. Appl Ergon2018; 70: 202–231.
185.
DorrianJRoachGDFletcherA, et al.The effects of fatigue on train handling during speed restrictions. Transp Res Part F Traffic Psychol Behav2006; 9: 243–257.
186.
HossainJLReinishLWKayumovL, et al.Underlying sleep pathology may cause chronic high fatigue in shift-workers. J Sleep Res2003; 12: 223–230.
187.
HicklinDSchwanderJ. Shift work and sleep. Praxis2019; 108: 119–124.
188.
PilcherJJCoplenMK. Work/rest cycles in railroad operations: effects of shorter than 24-h shift work schedules and on-call schedules on sleep. Ergonomics2000; 43: 573–588.
189.
RanjanRPrasadT. Literature review report on-An analytical study on working conditions of loco-pilots (Railway drivers) in India. Eur J Bus Manag2013; 5: 93–101.
190.
DorrianJChapmanJBowditchL, et al.A survey of train driver schedules, sleep, wellbeing, and driving performance in Australia and New Zealand. Sci Rep2022; 12: 1–11.
191.
StoynevAGMinkovaNK. Circadian rhythms of arterial pressure, heart rate and oral temperature in truck drivers. Occup Med1997; 47: 151–154.
192.
BrequeMDe NulLPetridisA. Industry 5.0: towards a sustainable, human-centric and resilient European industry. European Commission, Directorate-General for Research and Innovation.
193.
HuangSWangBLiX, et al.Industry 5.0 and society 5.0—Comparison, complementation and co-evolution. J Manuf Syst2022; 64: 424–428.
