Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance

Li, Rui; Verhagen, Wim J. C.; Curran, Richard

doi:10.1016/j.ast.2020.105877

Cited by 41 publications

(17 citation statements)

References 27 publications

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“…i a  , and if not, 0 i a  ) (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) where, N is the total number of sample data. Define l p as the recognition rate to category l:…”

Section: ) External Indexmentioning

confidence: 99%

“…In recent years, condition based maintenance (CBM) has been vigorously developed, compared with the traditional breakdown maintenance and preventive maintenance, the maintenance cost of the aircraft life cycle is reduced, and in which, the health status acquisition is very important. PHM [3][4][5][6] is an upgrade and development of CBM to meet the requirements of autonomous support and independent diagnosis. Usually there are three methods to realize PHM: model-based, datadriven and probability statistics, in which the data-driven method requires less prior knowledge of the target system.…”

Section: Introductionmentioning

confidence: 99%

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Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

Chen

Liu

et al. 2020

IEEE Access

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“…i a  , and if not, 0 i a  ) (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) where, N is the total number of sample data. Define l p as the recognition rate to category l:…”

Section: ) External Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

Chen

Liu

et al. 2020

IEEE Access

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“…An alternative practice to TBM would be to execute maintenance based on the real time health status of the aircraft, the so-called condition-based maintenance (CBM). CBM is a paradigm swift aiming to reliably assess the condition of the aircraft's systems and structures, confidently estimate the future health state and informatively support the operators for the decision making on when maintenance should be performed (Lee & Mitici, 2020), (Kallen & Noortwijk, 2005), (Li, Verhagen & Curran, 2020), (Ezhilarasu, Skaf & Jennions). The Advisory Council for Aeronautical Research in Europe (ACARE) envisages that, by 2050, all new aircraft will be designed for CBM and it is expected that CBM will contribute to a significant reduction in maintenance, repair and overhaul process time (ACARE, 2005).…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prognosis of Aircraft Brakes

Λούτας¹,

Oikonomou

Eleftheroglou³

et al. 2022

IJPHM

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We investigate the performance of three different data-driven prognostic methodologies towards the Remaining Useful Life estimation of commercial aircraft brakes being continuously monitored for wear. The first approach utilizes a probabilistic multi-state deterioration mathematical model i.e. a Hidden Semi Markov model whilst the second utilizes a nonlinear regression approach through classical Artificial Neural Networks in a Bootstrap fashion in order to obtain prediction intervals to accompany the mean remaining life estimates. The third approach attempts to leverage the highly linear degradation data over time and uses a simple linear regression in a Bayesian framework. All methodologies, when properly trained with historical degradation data, achieve excellent performance in terms of early and accurate prediction of the remaining useful flights that the monitored set of brakes can safely serve. The paper presents a real-world application where it is demonstrated that even in non-complex linear degradation data the inherent data stochasticity prohibits the use of a simple mathematical approaches and asks for methodologies with uncertainty quantification.

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“…However, to date, analogous safe quantities of mineral dust that can be tolerated by an engine have yet to be established. This inability to avoid frequent ingestion of airborne particulate combined with the impracticality of airspace closures has precipitated a shift towards model-driven, risk-based approaches in which certain levels of damage, performance loss and reductions in safety margin can be tolerated to maintain efficiency of operations [4]. Key to this is the ability to determine safe quantities of airborne particulate that can be ingested by an engine without compromising safety margins.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Predictions of Aero-Engine Performance Degradation Due to Particle Ingestion

et al. 2021

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Aero-engines, which encounter clouds of airborne particulate, experience reduced performance due to the deposition of particles on their high-pressure turbine nozzle guide vanes. The rate of this degradation depends on particle properties, engine operating state and the duration of exposure to the particle cloud, variables that are often unknown or poorly constrained, leading to uncertainty in model predictions. A novel method coupling one-dimensional gas turbine performance analysis with generalised predictions of particle deposition is developed and applied through the use of Monte Carlo simulations to better predict high-pressure turbine degradation. This enables a statistical analysis of deterioration from which mean performance losses and confidence intervals can be defined, allowing reductions in engine life and increased operational risk to be quantified. The method is demonstrated by replicating two particle cloud encounter events for the Rolls-Royce RB211-524C engine and is used to predict empirical particle properties by correlating measured engine performance data with Monte Carlo model inputs. Potential improvements in the confidence of these predictions due to more tightly constrained input and validation data are also demonstrated. Finally, the potential combination of the Monte Carlo coupled degradation model with in-service engine performance data and particle properties determined through remote or in situ sensing is outlined and its role in a digital twin to enable a predictive approach to operational capability is discussed.

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Toward a methodology of requirements definition for prognostics and health management system to support aircraft predictive maintenance

Cited by 41 publications

References 27 publications

Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

Health Monitoring of Landing Gear Retraction/Extension System Based on Optimized Fuzzy C-Means Algorithm

Remaining Useful Life Prognosis of Aircraft Brakes

Monte Carlo Predictions of Aero-Engine Performance Degradation Due to Particle Ingestion

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