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Vibration based fault detection and identification in an aircraft skeleton structure via a stochastic functional model based method

机译：基于随机功能模型的方法在飞机骨架结构中基于振动的故障检测与识别

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摘要

The problem of vibration based fault detection, identification (localization) and estimation in a scale aircraft skeleton structure is considered via a stochastic functional model based method (FMBM). The method is based on the novel class of stochastic Functionally Pooled models, which are capable of accurately representing the structure in a faulty state for the state's continuum of fault magnitudes, as well as interval estimation and formal statistical hypothesis testing procedures. The faults considered consist of small masses attached to the structure. The method is capable of operating even on single-excitation single-response signals, and is shown to achieve effective fault detection and identification, as well as remarkable accuracy in estimating the exact fault magnitude. This is so even for "unmodelled" faults, or faults monitored by remote sensors.
机译：通过基于随机功能模型的方法（FMBM），考虑了规模飞机骨架结构中基于振动的故障检测，识别（定位）和估计问题。该方法基于新颖的一类随机功能池模型，该模型能够针对状态的连续故障量级准确地表示处于故障状态的结构，以及区间估计和正式的统计假设检验程序。所考虑的断层由附着在结构上的小块组成。该方法甚至能够在单励磁单响应信号上运行，并且被证明可以实现有效的故障检测和识别，并且在估计准确的故障幅度方面具有显着的准确性。即使对于“未建模”故障或由远程传感器监视的故障，情况也是如此。

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