Fault Detection via Occupation Kernel Principal Component Analysis
Fault Detection via Occupation Kernel Principal Component Analysis
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DOI：
10.1109/lcsys.2023.3287568
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发表时间：
2023-03
影响因子：
3
通讯作者：
Zachary A. Morrison;Benjamin P. Russo;Yingzhao Lian;R. Kamalapurkar
中科院分区：
文献类型：
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作者：
Zachary A. Morrison;Benjamin P. Russo;Yingzhao Lian;R. Kamalapurkar
自动化系统的可靠运行在很大程度上取决于在底层动态中检测故障的能力。虽然传统的基于模型的方法已被广泛用于故障检测，但数据驱动方法由于易于部署和对专家知识的需求最小而受到越来越多的关注。在这封信中，我们提出了一种使用占用核的新型主成分分析（PCA）方法。占用内核导致的特征图，是定制的测量数据，具有固有的噪声鲁棒性，由于使用的集成，并可以利用不规则采样的系统轨迹的可变长度的PCA。利用占据核主元分析方法，提出了一种基于重构误差的故障检测方法，并通过数值仿真验证了该方法的有效性。
Reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamics. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need for expert knowledge. In this letter, we present a novel principal component analysis (PCA) method that uses occupation kernels. Occupation kernels result in feature maps that are tailored to the measured data, have inherent noise-robustness due to the use of integration, and can utilize irregularly sampled system trajectories of variable lengths for PCA. The occupation kernel PCA method is used to develop a reconstruction error approach to fault detection and its efficacy is validated using numerical simulations.