In-service aircraft engines turbine blades life prediction based on multi-modal operation and maintenance data

Liu, He; Sun, Junjie; Lei, Shiying; Ning, Shungang

doi:10.1016/j.jppr.2021.09.001

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…Conservatively, condition monitoring is a process of observing parameters that indicate the current status of the system [4]. It plays a significant role in the maintenance, management, and sustainable operations of various sectors, such as manufacturing industries [8], electronics [9], and transportation [10]. The execution of condition monitoring in these industries enables maintenance to be scheduled and actions to be taken to prevent consequential damages.…”

Section: Condition Monitoringmentioning

confidence: 99%

Digital Twin-Driven Machine Condition Monitoring: A Literature Review

et al. 2022

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Digital twin (DT), aiming to characterise behaviors of physical entities by leveraging the virtual replica in real time, is an emerging technology and paradigm at the forefront of the Industry 4.0 revolution. The implementation of DT in predictive maintenance has facilitated its growth. As a major component of predictive maintenance, condition monitoring (CM) has great potential to combine with DT. To describe the state-of-the-art of DT-driven CM, this paper delivers a systematic review on the theoretical and practical development of DT in advancing CM. The evolution of concepts, main research areas, applied domains, and related key technologies are summarised. The driver of DT for CM is detailed in three aspects: data support, capability enhancement, and maintenance mode shift. The implementation process of DT-driven CM is introduced from the classification of DT modelling and the extension of monitoring algorithms. Finally, current challenges and opportunities for future research are discussed especially concerning the barriers and gaps in data management, high-fidelity modelling, behavior characterisation, framework standardisation, and uncertainty quantification.

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Section: Condition Monitoringmentioning

confidence: 99%

Digital Twin-Driven Machine Condition Monitoring: A Literature Review

et al. 2022

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“…established a Bayesian confidence network to predict blade life loss in combination with engine service parameters and atmospheric environment conditions in the operating area to assist in decision‐making blade repair levels 18 . Liu and Sun proposed an RUL prediction method for high‐pressure turbine blades based on the machine learning‐based mechanism with multiple information fusion 19 . The multi‐source information fusion method based on the finite element agent model, proposed by Pillai et al., used machine learning to fuse data including environmental data to further reduce the uncertainty of prediction results 20 .…”

Section: Introductionmentioning

confidence: 99%

“…18 Liu and Sun proposed an RUL prediction method for high-pressure turbine blades based on the machine learning-based mechanism with multiple information fusion. 19 The multi-source information fusion method based on the finite element agent model, proposed by Pillai et al, used machine learning to fuse data including environmental data to further reduce the uncertainty of prediction results. 20 Liu and Liu established a life cycle cost model considering multiple dependent degradation processes and environmental influence.…”

Section: Introductionmentioning

confidence: 99%

A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data

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Sun

Chen

et al. 2023

Quality & Reliability Eng

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The data measured by the pitot tube (PT) is related to airspeed indication and flight safety. However, there are few methods to solve the problem of failure prediction and predictive maintenance of PT. This study proposes a method for predicting the remaining useful life (RUL) of the PT based on dynamic operating data. First, an exposure index (EI) characterizing the severity level of operating conditions is proposed based on multiple dynamic operating and environmental parameters, then a cumulative exposure model (CEM) is developed to calculate the cumulative exposure. Dynamic covariate information is incorporated into the time-to-failure distribution of PT through the CEM, the RUL distribution of the individual PT is then obtained by Monte Carlo simulation. A case study is carried out based on actual data of a commercial aircraft fleet and shows that the proposed CEM can effectively use historical aircraft environmental information and truncated failure data of the components. The relative error of the remaining life prediction is substantially improved compared to the traditional reliability analysis methods, which means that the proposed model can provide more reliable RUL results based on historical operating conditions, providing better support for PT risk assessment and condition-based maintenance decisions.

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Section: Introductionmentioning

confidence: 99%

“…Due to the high radius of the structure and the large mass of the blade, the gas turbine blades are subjected to huge mechanical loads, high temperature loads, aerodynamic loads, etc., and it is difficult to design the structural strength and life. Domestic scholars [2][3][4][5][6][7][8][9][10] have carried out certain research work on gas turbine blade structure design and life analyzer technology.Gas turbine blades in aero-engines experience complex loadings, depending on the location of the structure, design method and service. Therefore, the failure of the blade can be result from low cycle fatigue [2][3] , creep-fatigue, thermal fatigue [9] and thermal mechanical fatigue [10] .…”

mentioning

confidence: 99%