A digital twin-enhanced collaborative maintenance paradigm for aero-engine fleet

Jiawei REN, Ying CHENG, Yingfeng ZHANG, Fei TAO

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PDF(1960 KB)
Front. Eng ›› 2024, Vol. 11 ›› Issue (2) : 356-361. DOI: 10.1007/s42524-024-0299-z
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A digital twin-enhanced collaborative maintenance paradigm for aero-engine fleet

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Abstract

Maintenance of aero-engine fleets is crucial for the efficiency, safety, and reliability of the aviation industry. With the increasing demand for air transportation, maintaining high-performing aero-engines has become significant. Collaborative maintenance, specifically targeting aero-engine fleets, involves the coordination of multiple tasks and resources to enhance management efficiency and reduce costs. Digital Twin (DT) technology provides essential technical support for the intelligent operation and maintenance of aero-engine fleets. DT maps physical object properties to the virtual world, creating high-fidelity, dynamic models. However, DT-enhanced collaborative maintenance faces various challenges, including the construction of complex system-layer DT models, management of massive integrated DT data, and the development of fusion mechanisms and decision-making methods for DT data and models. Overcoming these challenges will allow the aviation industry to optimize aero-engine fleet maintenance, ensuring safety, efficiency, and cost-effectiveness while meeting the growing demand for air transportation.

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aero-engine fleet / collaborative maintenance / Digital Twin (DT) / complex system

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Jiawei REN, Ying CHENG, Yingfeng ZHANG, Fei TAO. A digital twin-enhanced collaborative maintenance paradigm for aero-engine fleet. Front. Eng, 2024, 11(2): 356‒361 https://doi.org/10.1007/s42524-024-0299-z

References

[1]
ChenBLi CLiYWangA (2009). Reliability analysis method of an aircraft engine FADEC system. In: Proceedings of 2009 8th International Conference on Reliability, Maintainability and Safety, I and II: 289
[2]
Gavranis, A Kozanidis, G (2017). Mixed integer biobjective quadratic programming for maximum-value minimum-variability fleet availability of a unit of mission aircraft. Computers & Industrial Engineering, 110: 13–29
CrossRef Google scholar
[3]
GlaessgenEStargel D (2012). The digital twin paradigm for future NASA and U.S. Air Force vehicles. In 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. American Institute of Aeronautics and Astronautics
[4]
GrievesMVickers J (2017). Digital twin: mitigating unpredictable, undesirable emergent behavior in complex system. In: Grieves M, Vickers J, eds. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches. Cham: Springer International Publishing: 85–113
[5]
Jenab, K Zolfaghari, S (2008). A virtual collaborative maintenance architecture for manufacturing enterprises. Journal of Intelligent Manufacturing, 19( 6): 763–771
CrossRef Google scholar
[6]
Liu, J (2020). A dynamic modelling method of a rotor-roller bearing-housing system with a localized fault including the additional excitation zone. Journal of Sound and Vibration, 469: 115144
CrossRef Google scholar
[7]
QianAXing HBoPTangLQiuR (2022). Spatial-temporal data analysis of digital twin//Digital Twin | Open Access Publishing Platform
[8]
Qi, H Lu, Y Song, S Xu, Q (2022). Fatigue reliability analysis system for key components of aero-engine. International Journal of Aerospace Engineering, 2022: e1143901
CrossRef Google scholar
[9]
Rath, N Mishra, R K Kushari, A (2023). aero-engine health monitoring, diagnostics and prognostics for condition-based maintenance: An overview. International Journal of Turbo & Jet-Engines, 40( s1): s279–s292
CrossRef Google scholar
[10]
Safaei, N Banjevic, D Jardine, A K S (2011). Workforce-constrained maintenance scheduling for military aircraft fleet: A case study. Annals of Operations Research, 186( 1): 295–316
CrossRef Google scholar
[11]
Sun, C He, Z Cao, H Zhang, Z Chen, X Zuo, M (2015). A non-probabilistic metric derived from condition information for operational reliability assessment of aero-engines. IEEE Transactions on Reliability, 64( 1): 167–181
CrossRef Google scholar
[12]
Sun, J Zuo, H Liang, K Chen, Z (2016). Bayesian network-based multiple sources information fusion mechanism for gas path analysis. Journal of Propulsion and Power, 32( 3): 611–619
CrossRef Google scholar
[13]
Sun, J Zuo, H Liu, P Wen, Z (2013). Experimental study on engine gas-path component fault monitoring using exhaust gas electrostatic signal. Measurement Science & Technology, 24( 12): 125107
CrossRef Google scholar
[14]
Tahan, M Tsoutsanis, E Muhammad, M Abdul Karim, Z A (2017). Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review. Applied Energy, 198: 122–144
CrossRef Google scholar
[15]
Tao, F Cheng, J Qi, Q Zhang, M Zhang, H Sui, F (2018). Digital twin-driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94( 9–12): 3563–3576
CrossRef Google scholar
[16]
Tao, F Qi, Q (2019). Make more digital twins. Nature, 573( 7775): 490–491
CrossRef Google scholar
[17]
Tao, F Sun, X Cheng, J Zhu, Y Liu, W Wang, Y Xu, H Hu, T Liu, X Liu, T Sun, Z Xu, J Bao, J Xiang, F Jin, X (2024). makeTwin: A reference architecture for digital twin software platform. Chinese Journal of Aeronautics, 37( 1): 1–18
CrossRef Google scholar
[18]
Tao, F Xiao, B Qi, Q Cheng, J Ji, P (2022). Digital twin modeling. Journal of Manufacturing Systems, 64: 372–389
CrossRef Google scholar
[19]
Tao, F Zhang, H Liu, A Nee, A Y C (2019). Digital twin in industry: State-of-the-Art. IEEE Transactions on Industrial Informatics, 15( 4): 2405–2415
CrossRef Google scholar
[20]
TaoFZhangM (2017). Digital twin shop-floor: A new shop-floor paradigm towards smart manufacturing. IEEE Access: Practical Innovations, Open Solutions, 5: 20418–20427
[21]
Wang, H Liu, L Fei, Y Liu, T (2016). A collaborative manufacturing execution system oriented to discrete manufacturing enterprises. Concurrent Engineering-Research and Applications, 24( 4): 330–343
CrossRef Google scholar
[22]
WangYWang XTaoFLiuA (2021). Digital twin-driven complexity management in intelligent manufacturing//Digital Twin | Open Access Publishing Platform
[23]
ZhangMTao FHuangBWangLAnwerN NeeA Y C (2021). Digital twin data: methods and key technologies//Digital Twin | Open Access Publishing Platform
[24]
Zhang, Y Xin, Y Liu, Z Chi, M Ma, G (2022). Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE. Reliability Engineering & System Safety, 220: 108263
CrossRef Google scholar
[25]
Zhao, Y Chen, Y (2022). Extreme learning machine based transfer learning for aero-engine fault diagnosis. Aerospace Science and Technology, 121: 107311
CrossRef Google scholar
[26]
Zhu, R Liang, Q Zhan, H (2017). Analysis of aero-engine performance and selection based on fuzzy comprehensive evaluation. Procedia Engineering, 174: 1202–1207
CrossRef Google scholar

Competing Interests

The authors declare that they have no competing interests.

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