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AUTOMATED HEALTH MANAGEMENT FOR GAS TURBINE ENGINE ACCESSORY SYSTEM COMPONENTS

机译:燃气涡轮发动机配件系统组件自动化健康管理

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The authors have developed model-based and data-driven techniques to provide reliable health assessments of hydraulic pumps and valves, which are essential components in aircraft fuel and lubrication systems. As part of this effort, a physical model of an aircraft fuel/lubrication system was created and parameters indicative of pump and valve faults were derived. Data-driven routines were also developed to analyze dynamic pressure signals and detect faults that would be difficult to incorporate into physical models. Evolutionary prognostics methods were also implemented to track faults across feature space and indicate time to failure for each component. The approach was demonstrated using an experimental setup representative of aircraft fuel and lubrication systems. Pump leakage, pump gear damage, and valve blockage were seeded on the setup, and the developed routines were trained with experimental data. The developed routines yielded parameters that were uniquely correlated to particular faults. These parameters allowed wide separation between baseline and faulted cases, yielding negligible missed detection rates for moderate faults and reasonable missed detection rates for an incipient valve blockage fault. The demonstration produced a quantifiable estimate of achievable performance using the hybrid techniques compared to traditional performance monitoring on accessory components.
机译:作者开发了基于模型和数据驱动的技术,以提供液压泵和阀门的可靠健康评估,这是飞机燃料和润滑系统的必要组件。作为这一努力的一部分,创建了飞机燃料/润滑系统的物理模型,推导了指示泵和阀故障的参数。还开发了数据驱动的例程来分析动态压力信号并检测难以包含在物理模型中的故障。还实施了进化预测方法以跟踪功能空间的故障,并指示每个组件的失败时间。使用代表飞机燃料和润滑系统的实验设置来证明该方法。在设置上播种泵泄漏,泵齿轮损坏和阀门堵塞,并且发达的例程进行了实验数据培训。发达的例程产生了与特定故障唯一相关的参数。这些参数允许基线和故障情况之间的广泛分离,产生可忽略的错失检测速率,适用于初始故障,合理的未错过检测速率进行初始阀门阻塞故障。与在附件组件上的传统性能监测相比,该示范产生了使用混合技术的可实现性能的可量化估计。

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