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Engine health monitoring (EHM) system for advanced diagnostic monitoring of gas turbine engines

机译：发动机健康监测（EHM）燃气轮机先进诊断监测系统

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Advancing the USAF's capabilities in engine life measurement and diagnostic monitoring of critical engine components is necessary to improve engine availability, minimize performance degradation, and reduce life cycle costs. Proven artificial intelligence (AI) technologies such as neural networks, fuzzy logic and expert systems present an opportunity to significantly enhance current trending and diagnostic capabilities in a real-time monitoring environment. This paper outlines the strategy adopted by the USAF to develop a state-of-the-art engine health monitoring system. In addition, the status of an R&D program whose ultimate aim is to demonstrate the perceived capability is also discussed. Engine data currently sensed and recorded for post flight processing will be analyzed in a continuous real-time mode. For fault detection and accommodation, extensive knowledge of how a healthy engine operates under given conditions will be analyzed, and any deviation from this "normal" pattern of expected parameters will be detected and further analyzed. Faults resulting from sensor failure modes will be promptly isolated and more complex faults will be identified by reasoning utilizing fuzzy logic and pattern recognition schemes. The same sensed data will be used as inputs to the life measurement module of the monitoring system where life usage algorithms will determine critical component remaining life based on actual mission severity. The system under development will be based on the Rolls-Royce T45 engine (Adour) which is fitted to the Navy's F-405 trainer, and a full-scale demonstration of the technology will ultimately be conducted on this engine.
机译：在发动机寿命中推进USAF的能力和对关键发动机组件的诊断监控是提高发动机可用性的必要条件，最大限度地减少性能下降，降低生命周期成本。经过验证的人工智能（AI）技术，如神经网络，模糊逻辑和专家系统，提供了在实时监测环境中显着提高当前趋势和诊断能力的机会。本文概述了美国空军通过开发最先进的发动机健康监测系统的策略。此外，还讨论了其最终目标是展示感知能力的研发计划的状态。将以连续的实时模式分析当前感测和记录的发动机数据。对于故障检测和容纳，将分析对健康发动机在给定条件下如何运行的广泛了解，并且将检测和进一步分析来自这种“正常”的预期参数模式的任何偏差。由传感器故障模式产生的故障将会及时隔离，并且通过使用模糊逻辑和模式识别方案来识别更复杂的故障。相同的感测数据将被用作监控系统的生命测量模块的输入，其中生命使用算法将根据实际任务严重性确定关键组件剩余寿命。开发中的系统将基于卷筒罗伊斯T45发动机（Adour），该发动机（Adour）安装在海军的F-405培训师身上，并且最终将在该发动机上进行该技术的全面示范。

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