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Coupling Principal Component Analysis And Kalman Filtering Algorithms For On-line Aircraft Engine Diagnostics

机译：在线飞机发动机诊断的耦合主成分分析和卡尔曼滤波算法

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摘要

Engine health monitoring has been an area of intensive research for more than three decades. Numerous methods have been developed with the goal of performing an accurate assessment of the engine condition. It is generally accepted that a practical implementation of a monitoring tool will rely on a combination of several techniques.rnIn this framework, the present contribution proposes an original approach for coupling two diagnostic tools in order to enhance the capability of an engine health monitoring system. One tool is based on a principal component analysis scheme and the other is based on a Kalman filter technique. The three methodologies are compared and the benefit of the combined tool is demonstrated on simulated fault cases which can be expected in a commercial turbofan layout.
机译：过去三十多年以来，发动机健康监测一直是深入研究的领域。为了执行对发动机状况的精确评估的目标，已经开发了许多方法。公认的是，监视工具的实际实现将依赖于几种技术的组合。在此框架中，本文稿提出了一种将两个诊断工具耦合的原始方法，以增强发动机运行状况监视系统的能力。一种工具基于主成分分析方案，另一种基于卡尔曼滤波技术。比较了这三种方法，并在商业涡轮风扇布局中可以预期的模拟故障情况下证明了组合工具的优势。

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