As an advanced technology driven by the Fourth Industrial Revolution (Industry 4.0), Digital Twin (DT) has generated increasing attention and has been widely adopted across various sectors, including aerospace, healthcare and manufacturing. Despite the significant progress in both theoretical development and practical applications, notable challenges remain in applying DT to prognostics and health management (PHM), particularly in the case of rotating machinery. This article reviews the existing literature on DTs, clarifying its concept and applications across different fields. The implementation of DT for rotating machinery is analysed with the use of a five-dimensional model, with detailed discussions on the specific techniques used in each stage. Additionally, the role of DTs in each phase of a PHM process is analysed, aiming to assess its effectiveness and contribution to enhance PHM workflows. Current applications of DT integration in anomaly detection, fault diagnosis and remaining useful life prediction are also examined, emphasizing the advantages of DT in overcoming the limitations of traditional PHM approaches. Furthermore, the article addresses the urgent challenges facing the DT adoption, proposes potential solutions and offers potential research perspectives to further advance its application in rotating machinery systems.
GrievesM.Digital twin: manufacturing excellence through virtual factory replication. White Paper2014; 1(2014): 1–7.
3.
TaoFZhangMLiuY, et al. Digital twin driven prognostics and health management for complex equipment. CIRP Ann2018; 67(1): 169–172.
4.
ShaftoMConroyMDoyleR, et al. Draft modeling, simulation, information technology & processing roadmap, Technol Area2010; 11: 1–32.
5.
VachálekJBartalskýLRovnýO, et al. The digital twin of an industrial production line within the industry 4.0 concept. In: 2017 21st International conference on process control (PC), June 2017, pp. 258–262.
6.
DeanMCOedingJFDinizP, et al. Leveraging digital twins for improved orthopaedic evaluation and treatment. J Exp Orthop2024; 11(4): e70084.
7.
HanYLiYLiY, et al. Digital twinning for smart hospital operations: framework and proof of concept. Technol Soc2023; 74: 102317.
8.
LiYShenXZhouC.Dynamic multi-turbines spatiotemporal correlation model enabled digital twin technology for real-time wind speed prediction. Renew Energy2023; 203: 841–853.
9.
SongHSongMLiuX.Online autonomous calibration of digital twins using machine learning with application to nuclear power plants. Appl Energy2022; 326: 119995.
10.
LinLAthePRouxelinP, et al. Development and assessment of a nearly autonomous management and control system for advanced reactors. Ann Nucl Energy2021; 150: 107861.
11.
ShahatEHyunCTYeomC.City digital twin potentials: a review and research agenda. Sustainability2021; 13(6): 3386.
12.
MežaSMauko PranjićAVezočnikR, et al. Digital twins and road construction using secondary raw materials. J Adv Transp2021; 2021(1): 8833058.
ZioE.Prognostics and Health Management (PHM): where are we and where do we (need to) go in theory and practice. Reliab Eng Syst Saf2022; 218: 108119.
15.
GharibHKovácsG.A review of prognostic and health management (PHM) methods and limitations for marine diesel engines: new research directions. Machines2023; 11(7): 695.
16.
TaoFSunXChengJ, et al. MakeTwin: a reference architecture for digital twin software platform. Chin J Aeronaut2024; 37(1): 1–18.
17.
BooyseWWilkeDNHeynsS.Deep digital twins for detection, diagnostics and prognostics. Mech Syst Signal Process2020; 140: 106612.
18.
PengC-CChenY-H.Fixed-wing unmanned aerial vehicle rotary engine anomaly detection via online digital twin methods. IEEE Trans Aerosp Electron Syst2024; 60(1): 741–758.
19.
WangK-JLeeY-HAngelicaS. Digital twin design for real-time monitoring – a case study of die cutting machine. Int J Prod Res 20221; 59(21): 6471–6485.
20.
LiuZLangZ-QGuiY, et al. Digital twin-based anomaly detection for real-time tool condition monitoring in machining. J Manuf Syst2024; 75: 163–173.
21.
de López DizSLópezRMSánchezFJR, et al. A real-time digital twin approach on three-phase power converters applied to condition monitoring. Appl Energy2023; 334: 120606.
22.
XiongMWangHFuQ, et al. Digital twin–driven aero-engine intelligent predictive maintenance. Int J Adv Manuf Technol2021; 114(11): 3751–3761.
23.
QinYWuXLuoJ.Data-model combined driven digital twin of life-cycle rolling bearing. IEEE Trans Industr Inform2022; 18(3): 1530–1540.
24.
SonSLeeHJeongD, et al. A novel physics-informed neural network for modeling electromagnetism of a permanent magnet synchronous motor. Adv Eng Inform2023; 57: 102035.
25.
MoghadamFKNejadAR.Online condition monitoring of floating wind turbines drivetrain by means of digital twin. Mech Syst Signal Process2022; 162: 108087.
26.
LiYShenX.A novel wind speed-sensing methodology for wind turbines based on digital twin technology. IEEE Trans Instrum Meas2022; 71: 1–13.
27.
YucesanYAVianaFAC. Physics-informed digital twin for wind turbine main bearing fatigue: quantifying uncertainty in grease degradation. Appl Soft Comput2023; 149: 110921.
28.
ZhangJWangZLiS, et al. A digital twin approach for gas turbine performance based on deep multi-model fusion. Appl Therm Eng2024; 246: 122954.
29.
MiSFengYZhengH, et al. Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework. J Manuf Syst2021; 58: 329–345.
30.
Diesel engine rebuild kits parts and heavy duty pros [Online], http://www.heavydutypros.com/ (accessed 4 February 2025).
KajiMParvizianJ.devanVennHW.Constructing a reliable health indicator for bearings using convolutional autoencoder and continuous wavelet transform. Appl Sci2020; 10(24): 8948.
35.
XuZGaoTLiZ, et al. Digital twin modeling method for hierarchical stiffened plate based on transfer learning. Aerospace2023; 10(1): 66.
36.
ZhaoWZhangCWangJ, et al. Research on digital twin driven rolling bearing model-data fusion life prediction method. IEEE Access2023; 11: 48611–48627.
37.
WangWGuoHLiX, et al. Deep learning for assessment of environmental satisfaction using BIM big data in energy efficient building digital twins. Sustain Energy Technol Assess2022; 50: 101897.
38.
XiaPHuangYTaoZ, et al. A digital twin-enhanced semi-supervised framework for motor fault diagnosis based on phase-contrastive current dot pattern. Reliab Eng Syst Saf2023; 235: 109256.
39.
YanSZhongXShaoH, et al. Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization. Reliab Eng Syst Saf2023: 239: 109522.
40.
MaLJiangBXiaoL, et al. Digital twin-assisted enhanced meta-transfer learning for rolling bearing fault diagnosis. Mech Syst Signal Process2023; 200: 110490.
41.
YuJWangSWangL, et al. Gearbox fault diagnosis based on a fusion model of virtual physical model and data-driven method. Mech Syst Signal Process2023; 188: 109980.
42.
HuWWangTChuF.Novel Ramanujan digital twin for motor periodic fault monitoring and detection. IEEE Trans Ind Inform2023; 19(12): 11564–11572.
43.
LiuXJinYYangF, et al. Twin data multimode collaborative transfer learning for bearing failure diagnosis. Eng Appl Artif Intell2024; 137: 109142.
44.
HartwellAMontanaFJacobsW, et al. Distributed digital twins for health monitoring: resource constrained aero-engine fleet management. Aeronaut J2024; 128(1325): 1556–1575.
45.
ZhouLWangHXuS.Aero-engine gas path system health assessment based on depth digital twin. Eng Fail Anal2022; 142: 106790.
46.
PengC-CChenY-H.Digital Twins-based online monitoring of TFE-731 Turbofan engine using fast orthogonal search. IEEE Syst J2022; 16(2): 3060–3071.
47.
FengCLiuCJiangD.Root cause localization for wind turbines using physics guided multivariate graphical modeling and fault propagation analysis. Knowl Based Syst2024; 295: 111838.
48.
PiltanFKimJ-M.Bearing anomaly recognition using an intelligent digital twin integrated with machine learning. Appl Sci2021; 11(10): 4602.
49.
PiltanFKimC-HKimJ-M.Bearing crack diagnosis using a smooth sliding digital twin to overcome fluctuations arising in unknown conditions. Appl Sci2022; 12(13): 6770.
50.
WangXYanJXuX.A novel dynamic spatio-temporal graph based condition monitoring framework for consistency retention of digital twin. J Manuf Syst2025; 79: 455–465.
51.
BoYWuHCheW, et al. Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition. Eng Appl Artif Intell2024; 131: 107853.
52.
HuangYYuanBXuS, et al. Fault diagnosis of permanent magnet synchronous motor of coal mine belt conveyor based on digital twin and ISSA-RF. Processes2022; 10(9): 1679.
53.
ChenZLiangDJiaS, et al. Incipient interturn short-circuit fault diagnosis of permanent magnet synchronous motors based on the data-driven digital twin model. IEEE J Emerg Sel Top Power Electron2023; 11(3): 3514–3524.
54.
PatilCTheotokatosGWuY, et al. Investigation of logarithmic signatures for feature extraction and application to marine engine fault diagnosis. Eng Appl Artif Intell2024; 138: 109299.
55.
PengboZRenxiangCDongL, et al. Digital twin-assisted fault diagnosis of induction motors with thermal analysis of inter-turn short circuit faults. Meas Sci Technol2025; 36(6): 066118.
56.
MenCHanYWangP, et al. The interpretable reasoning and intelligent decision-making based on event knowledge graph with LLMs in fault diagnosis scenarios. IEEE Trans Instrum Meas2025; 74: 1–16.
57.
HanWChengLHanW, et al. Adaptive update method of digital twin for DC-DC converter based on mechanism-data drive. IEEE Sens J2025; 25(8): 14147–14157.
58.
TseliosINikolakopoulosPG.Integrated artificial intelligent and physics-based models for unbalance estimation in rotating systems. J Vib Eng Technol2025; 13(1): 31.
59.
PengBBiYXueB, et al. A survey on fault diagnosis of rolling bearings. Algorithms2022; 15(10): 347.
60.
LiSJiangQXuY, et al. Digital twin-driven focal modulation-based convolutional network for intelligent fault diagnosis. Reliab Eng Syst Saf2023; 240: 109590.
61.
LiSJiangQXuY, et al. Digital twin-assisted interpretable transfer learning: a novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain. Adv Eng Inform2024; 62: 102681.
62.
ZhangYJiJCRenZ, et al. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliab Eng Syst Saf2023; 234: 109186.
63.
XuYJiangQLiS, et al. Digital twin-driven discriminative graph learning networks for cross-domain bearing fault recognition. Comput Ind Eng2024; 193: 110292.
64.
WangSSunWLiuH, et al. Research on wind turbine main shaft bearing fault diagnosis method based on unity 3D and transfer learning. Appl Sci2025; 15(4): 2003.
65.
FanCWangPZhangY, et al. Digital twin assisted degradation assessment of bearing cage performance. IEEE Trans Ind Inform2025; 21(7): 5171–5181.
66.
HuangXXieTLuoS, et al. Incremental learning with multi-fidelity information fusion for digital twin-driven bearing fault diagnosis. Eng Appl Artif Intell2024; 133: 108212.
67.
LiZDingXSongZ, et al. Digital twin-assisted dual transfer: a novel information-model adaptation method for rolling bearing fault diagnosis. Inform Fusion2024; 106: 102271.
68.
ZhuY, et al. Data generation approach based on data model fusion: an application for rolling bearings fault diagnosis with small samples. IEEE Trans Instrum Meas2025; 74: 1–16.
69.
ZhangXWangXYaoH, et al. Multi-similarity and gradient fusion digital twins for fault detection and diagnosis of rolling bearings. Comput Ind2025; 168: 104273.
70.
XiaQYueJChenJ, et al. Data and mechanism modeling: residual life start–end determination for systems with stable equilibrium state. IEEE Trans Instrum Meas2024; 73: 1–11.
71.
VettoriSDi LorenzoEPeetersB, et al. An adaptive-noise Augmented Kalman Filter approach for input-state estimation in structural dynamics. Mech Syst Signal Process2023; 184: 109654.
72.
LiCSunJWangB, et al. In-service load calculation surrogate models for high-pressure turbine blade life digital twin. Int J Turbo Jet-Engines2024; 41(3): 463–476.
73.
MaoHLiuZQiuC, et al. Prescriptive maintenance for complex products with digital twin considering production planning and resource constraints. Meas Sci Technol2023; 34(12): 125903.
74.
FengKJiJCZhangY, et al. Digital twin-driven intelligent assessment of gear surface degradation. Mech Syst Signal Process2023; 186: 109896.
75.
ZhaoW, et al. Research on rolling bearing virtual-real fusion life prediction with digital twin. Mech Syst Signal Process2023; 198: 110434.
76.
HuWWangTChuF.A faulty simulation model guided Ramanujan Digital twin architecture for rotating machine health monitoring. ISA Trans2025; 157: 408–418.
77.
FanJZhaoLLiM.Residual life prediction of rolling bearings driven by digital twins. Symmetry2025; 17(3): 406.
78.
WangJXiaoLLiuX.Rapid deployment of digital twin for life prediction of rolling bearings. Appl Intell2025; 55(7): 666.
79.
HeDZhaoJJinZ, et al. Prediction of bearing remaining useful life based on a two-stage updated digital twin. Adv Eng Inform2025; 65: 103123.
80.
CuiLXiaoYLiuD, et al. Digital twin-driven graph domain adaptation neural network for remaining useful life prediction of rolling bearing. Reliab Eng Syst Saf2024; 245: 109991.
81.
CuiLWangXLiuD, et al. An adaptive sparse graph learning method based on digital twin dictionary for remaining useful life prediction of rolling element bearings. IEEE Trans Ind Inform2024; 20(9): 10892–10900.
82.
DaiJTianLHanT, et al. Digital Twin for wear degradation of sliding bearing based on PFENN. Adv Eng Inform2024; 61: 102512.
