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EVIDENTIAL REASONING APPLIED TO MODEL-BASED DIAGNOSTICS IN LANDING GEAR ACTUATORS

机译：证据推理在起落架执行器中基于模型的诊断中的应用

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摘要

Replacing the heavy, bulky hydraulic actuators which extend and retract most landing gears with smaller and cleaner electromechanical counterparts, there is potential to significantly reduce landing gear mass. In aerospace new technologies must be guaranteed to be as reliable as the preceding system. For this reason additional support systems must be developed in order to monitor actuator performance. Such a system would provide important information useful in detecting and diagnosing developing faults that may lead to in-flight actuator failures, impacting upon the safe operation of the landing gears. This information can also aid in helping improve the availability of the aircraft by optimizing the time spent in maintenance operations. Design requirements such as minimal weight however restrict the choice of health monitoring approach for this application. The combining of data, knowledge and models within a data fusion framework can give robust health monitoring solutions and is the approach currently being applied to this application. This paper discusses applying evidential reasoning techniques to combine multiple residuals in order to generate fault symptoms for model-based diagnostics. This is demonstrated through simulated examples of the landing gear actuator system.
机译：用更小，更清洁的机电装置代替笨重，笨重的液压致动器，它们可以使大多数起落架伸出和缩回，从而有可能显着减少起落架质量。在航空航天领域，必须保证新技术与之前的系统一样可靠。因此，必须开发其他支持系统以监视执行器性能。这样的系统将提供重要的信息，可用于检测和诊断正在发展的故障，这些故障可能导致飞行中的执行器故障，从而影响起落架的安全运行。该信息还可以通过优化维护操作所花费的时间来帮助提高飞机的可用性。但是，设计要求（例如最小的重量）限制了此应用程序对健康监控方法的选择。数据融合框架中的数据，知识和模型的组合可以提供强大的健康监控解决方案，并且是当前正在应用于此应用程序的方法。本文讨论了应用证据推理技术来组合多个残差以生成基于模型诊断的故障症状。通过起落架执行器系统的模拟示例可以证明这一点。

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