A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems
A data-driven approach to actuator and sensor fault detection, isolation and estimation in discrete-time linear systems
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DOI：
10.1016/j.automatica.2017.07.040
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
E. Naderi;K. Khorasani
中科院分区：
文献类型：
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作者：
E. Naderi;K. Khorasani
在这项工作中，我们提出并开发了数据驱动的显式基于状态空间的故障检测、隔离和估计滤波器，这些滤波器仅通过可用的系统输入输出（I/O）测量和仅通过估计的系统马尔可夫参数直接识别和构建。所提出的方法不涉及约简步骤，也不需要识别系统扩展可观察性矩阵或其左零空间。我们提出的滤波器的性能与马尔可夫参数识别误差直接相关并线性依赖。估计过滤器使用设计人员选择的系统I/O数据子集进行操作。结果表明，只要所选子系统具有稳定的逆，我们所提出的滤波器通过调用低阶滤波器提供渐近无偏估计。我们推导了马尔可夫参数识别误差的估计误差动态，并表明它们可以直接从健康的系统I/O数据中合成。因此，我们提出的方法确保了估计误差可以有效地补偿。最后，我们提供了几个说明性的案例研究模拟，与文献中可用的方法相比，证明并确认了我们提出的方案的优点。
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.