Subspace aided data-driven design of robust fault detection and isolation systems
Subspace aided data-driven design of robust fault detection and isolation systems
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DOI：
10.1016/j.automatica.2011.05.028
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发表时间：
2011-11
期刊：
影响因子：
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通讯作者：
Yulei Wang;G. Ma;S. Ding;Chuanjiang Li
中科院分区：
文献类型：
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作者：
Yulei Wang;G. Ma;S. Ding;Chuanjiang Li
对子空间方法辅助数据驱动的鲁棒故障检测与隔离系统设计进行了研究。其基本思想是直接从测试数据中识别残差生成器的一种基本形式，然后利用性能指标来统一设计不同类型的稳健残差。提出了设计故障检测、隔离和识别残差发电机的四种算法。它们都能实现对未知输入的鲁棒性和对传感器或执行器故障的敏感度。简要分析了它们的存在条件和多故障识别问题，并以车辆横向动力学系统的仿真研究为例说明了该方法的应用。
This paper deals with subspace method aided data-driven design of robust fault detection and isolation systems. The basic idea is to identify a primary form of residual generators directly from test data and then make use of performance indices to make uniform the design of different type robust residuals. Four algorithms are proposed to design fault detection, isolation and identification residual generators. Each of them can achieve robustness to unknown inputs and sensitivity to sensor or actuator faults. Their existence conditions and multi-fault identification problem are briefly analyzed as well and the application of the method proposed is illustrated by a simulation study on the vehicle lateral dynamic system.