Addressing the current issue of neglecting individual variability in degradation modeling based on physical laws, this paper introduces an approach for modeling the degradation of aero-engine component performance. This method is grounded in the linear independent increment process, allowing for the creation of a staged model to describe turbine efficiency degradation. Bayesian updates, coupled with Gibbs sampling algorithms, are employed to estimate parameter distributions. Using NASA’s simulation dataset, we conducted a quantitative comparison of the proposed model with two other prevalent degradation models. The analysis revealed a 2.1% and 7.2% decrease in the DIC value of the linear independent increment process-based degradation model compared to the other two methods. This underscores the model’s improved capability to elucidate the degradation patterns in aero-engine component performance.
TahanMTsoutsanisEMuhammadM, et al.Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: a review. Appl Energy2017; 198: 122–144. https://doi.org/10.1016/j.apenergy.2017.04.048
2.
HanachiHMechefskeCLiuJ, et al.Performance-based gas turbine health monitoring, diagnostics, and prognostics: a survey. IEEE Trans Rel2018; 67: 1340–1363. https://doi.org/10.1109/TR.2018.2822702
VolponiAJ. Gas turbine engine health management: past, present, and future trends. J Eng Gas Turbine Power2014; 136: 051201. https://doi.org/10.1115/1.4026126
5.
IATA. Airline maintenance cost executive commentary:FY2020. International Air Transport Association, 2021.
6.
PanMXuYGuB, et al.Fuzzy-set theoretic control design for aircraft engine hardware-in-the-loop testing: mismatched uncertainty and optimality. IEEE Trans Ind Electron2022; 69: 7223–7233. https://doi.org/10.1109/TIE.2021.3095784
7.
WeiZJafariSZhangS, et al.Hybrid Wiener model: an on-board approach using post-flight data for gas turbine aero-engines modelling. Appl Therm Eng2021; 184: 116350. https://doi.org/10.1016/j.applthermaleng.2020.116350
8.
WangCZhuZLuN, et al.A data-driven degradation prognostic strategy for aero-engine under various operational conditions. Neurocomputing2021; 462: 195–207. https://doi.org/10.1016/j.neucom.2021.07.080
9.
LiuJZhangMZuoH, et al.Remaining useful life prognostics for aeroengine based on superstatistics and information fusion. Chin J Aeronaut2014; 27: 1086–1096. https://doi.org/10.1016/j.cja.2014.08.013
10.
ZhangSZhaiQLiY. Degradation modeling and RUL prediction with Wiener process considering measurable and unobservable external impacts. Reliab Eng Syst Saf2023; 231: 109021. https://doi.org/10.1016/j.ress.2022.109021
11.
GuoQYinlongLTianxiangZ. Performance degradation prediction of aero-engine based on NonlinearWiener process. J Propuls Technol2021; 42: 1956–1963.
12.
WangXHuCZiqiangR, et al. Performance degradation modeling and remaining useful life prediction for aero-engine based on nonlinear Wiener process. Acta Aeronautica Astronautica Sinica. 2020; 41: 195–205.
13.
ZhouMYMiuKQSunJX, et al.Data-driven modeling of aero-engine performance degradation models. IEEE Access2024; 12(99): 150020–150031.
14.
LiuXGuoZYouL. Modeling and reliability evaluation on the aero-engine multivariate performance degradation based on data fusion[M] Mechanical and aerospace engineering. IOS Press, 2025, pp. 97–103.
15.
ZhangBGaoSZhengZ, et al. Aeroengine performance degradation prediction method considering operating conditions. KSII Transactions on Internet & Information Systems. 2023; 17(9): 2314–2333.
16.
FeiCWHanYJWenJR, et al.Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk. Propulsion and Power Research2024; 13(1): 12–25.
17.
FeiCWLiCLinJY, et al.Structural design of aeroengine radiators: state of the art and perspectives. Propulsion and Power Research2024; 13(3):319–334.
18.
Shu-gangZYing-qingGJunL. Research on aircraft engine component-level models based on GasTurb/MATLAB. J Aero Power2012; 27: 2850–2856.
19.
GrewalNS. Gas turbine engine performance deterioration modelling and analysis. Cranfield institute of technology, 1988.
20.
LuFHuangJQiuX. Application of multi-outputs LSSVR by PSO to the aero-engine model. J Syst Eng Electron2009; 20: 1153–1158.
21.
YunshengZJunHBaofengT, et al.Simulation of component deterioration effect on performance of high bypass ratio turbofan engine. J Nanjing Univ Aeronaut Astronaut2013; 45: 447–452.
PlaceCSStruttJEAllsoppK, et al.Reliability prediction of helicopter transmission systems using stress-strength interference with underlying damage accumulation. Qual Reliab Eng Int1999; 15: 69–78.
WangZBaoRLiuC, et al.A novel failure time estimation method for degradation analysis based on general nonlinear Wiener processes. Reliab Eng Syst Saf2023; 230: 108913. https://doi.org/10.1016/j.ress.2022.108913
26.
LiuWYangW-AYouY. Three-stage wiener-process-based model for remaining useful life prediction of a cutting tool in high-speed milling. Sensors2022; 22: 4763. https://doi.org/10.3390/s22134763
27.
SunSWangJWangJ, et al.Remaining useful life prediction of accessories for the conventional circuit breaker based on Wiener process. Chin J Sci Instrum2019; 40: 26–37.
28.
MaJCaiLLiaoG, et al.A multi-phase Wiener process-based degradation model with imperfect maintenance activities. Reliab Eng Syst Saf2023; 232: 109075. https://doi.org/10.1016/j.ress.2022.109075
29.
SunFLiHChengY, et al.Reliability analysis for a system experiencing dependent degradation processes and random shocks based on a nonlinear Wiener process model. Reliab Eng Syst Saf2021; 215: 107906. https://doi.org/10.1016/j.ress.2021.107906
30.
ChenXLiuZ. A long short-term memory neural network based Wiener process model for remaining useful life prediction. Reliab Eng Syst Saf2022; 226: 108651. https://doi.org/10.1016/j.ress.2022.108651
31.
ZhangZSiXHuC, et al.Degradation data analysis and remaining useful life estimation: a review on Wiener-process-based methods. Eur J Oper Res2018; 271: 775–796. https://doi.org/10.1016/j.ejor.2018.02.033
32.
Hui-minFQiongW. Analysis method for linear process with independent increments. J Aero Power2010; 25: 930–935.
33.
ChaoMAKulkarniCGoebelK, et al.Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics. Date2021; 6(1): 14.
34.
SpiegelhalterDJBestNGCarlinBP, et al.Bayesian measures of model complexity and fit. J Roy Stat Soc B2002; 64: 583–639. https://doi.org/10.1111/1467-9868.00353
