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A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems

机译：用于执行器和传感器故障检测，离散时间线性系统的隔离和估计的数据驱动方法

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摘要

In this work, we propose and develop data-driven explicit state-space based fault detection, isolation and estimation filters that are directly identified and constructed from only the available system input-output (I/O) measurements and through only the estimated system Markov parameters. The proposed methodology does not involve a reduction step and does not require identification of the system extended observability matrix or its left null space. The performance of our proposed filters is directly related to and linearly dependent on the Markov parameters identification errors. The estimation filters operate with a subset of the system I/O data that is selected by the designer. It is shown that our proposed filters provide an asymptotically unbiased estimate by invoking a low order filter as long as the selected subsystem has a stable inverse. We have derived the estimation error dynamics in terms of the Markov parameters identification errors and have shown that they can be directly synthesized from the healthy system I/O data. Consequently, our proposed methodology ensures that the estimation errors can be effectively compensated for. Finally, we have provided several illustrative case study simulations that demonstrate and confirm the merits of our proposed schemes as compared to methodologies that are available in the literature. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在这项工作中，我们提出并开发了基于数据驱动的显式状态空间的故障检测，隔离和估计过滤器，这些故障检测和仅从可用的系统输入 - 输出（I / O）测量和仅通过估计的系统Markov（I / O）测量而直接识别和构建参数。所提出的方法不涉及还原步骤，并且不需要识别系统扩展可观察性矩阵或其左无效空间。我们提出的过滤器的性能与Markov参数识别错误直接相关和线性相关。估计过滤器与设计者选择的系统I / O数据的子集进行操作。结果表明，只要选定的子系统具有稳定的逆，就通过调用低阶滤波器，我们提出的过滤器提供了渐近无偏见的估计。我们在Markov参数识别错误方面派生了估计错误动态，并且已经显示它们可以从健康的系统I / O数据直接合成。因此，我们提出的方法确保了可以有效地补偿估计误差。最后，我们提供了几种说明性案例研究模拟，其展示并确认了我们所提出的计划的优点，与文献中提供的方法相比。 （c）2017 Elsevier Ltd.保留所有权利。

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