Mr. Shuai Fu Profile

Shuai Fu

at Cranfield University

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Proceedings Article | 9 May 2024 Presentation + Paper

Aeronautics failure: a prognostic methodology based on the physics of failure and statistical approaches for predictive maintenance

Shuai Fu, Nicolas Avdelidis

Proceedings Volume 12952, 1295205 (2024) https://doi.org/10.1117/12.3018035

KEYWORDS: Failure analysis, Phase only filters, Physics, Data modeling, Safety, Reliability, Systems modeling, Sensors, Material fatigue, Analytics

Read Abstract +

In contemporary times, there has been a reduction in the length of product lifecycles, accompanied by a growing consumer preference for more complex offers. The reality poses numerous obstacles, and the existing methodologies are not viable in the long term. In recent years, there has been a notable surge in scholarly attention towards the domain of prognostics. Most research efforts have mostly focused on the prediction of the remaining useful life (RUL) of individual components. The dissemination of failure mechanisms can also involve several components, and a range of prognostic methods are utilised to detect and track them at different levels of the system. Once specific thresholds of deterioration have been reached, it becomes crucial to implement specific maintenance measures to mitigate the risk of a potential system failure. Therefore, it is crucial to understand how the mechanisms have and will be distributed while striving to perform certain maintenance methods to extend the RUL. The authors in this research introduce a prognostic methodology for predictive maintenance that relies on the physics of failure (PoF) approach and statistical method. The objective of this methodology is to employ knowledge of aircraft fuel system failure mechanisms and use datasets derived from laboratory experiments and simulation model conducted on aircraft fuel systems. The proposed methodology integrates sensor data and algorithms to assess the degradation of a system from its expected normal operational condition as well as to predict its RUL. The proposed methodology has a high degree of robustness and consistently produces trustworthy outcomes.

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