飞机维修平台健康管理--综述

Andrews Darfour Kwakye, I. Jennions, Cordelia Mattuvarkuzhali Ezhilarasu
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引用次数: 0

摘要

飞机健康管理在部件和系统两个层面都进行了研究。在某些飞机故障(如波音 777 的燃油结冰问题)的案例中,有证据表明,使用综合飞行器健康管理(IVHM)系统的平台方法有助于在故障演变成灾难性后果之前及早发现故障及其交互影响。本文从飞机维护的角度回顾了飞机健康管理。它强调了平台解决方案在早期诊断故障及其交互影响方面的潜力。本文对有关维护及其演变的现有文献进行了全面分析,深入探讨了人工智能(AI)技术在维护中的应用,解释了其应用背后的原理,并说明了人工智能的实施如何利用平台传感器数据加强故障检测。此外,它还讨论了计算严重性和临界指数(健康指数)如何与人工智能的使用相辅相成,为飞机部件提供维护信息,协助运营决策。
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Platform health management for aircraft maintenance – a review
Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions.
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