Fault Detection via Occupation Kernel Principal Component Analysis

• URL: http://arxiv.org/abs/2303.11138v2
• Date: Mon, 26 Jun 2023 15:16:42 GMT
• Title: Fault Detection via Occupation Kernel Principal Component Analysis
• Authors: Zachary Morrison, Benjamin P. Russo, Yingzhao Lian, and Rushikesh Kamalapurkar
• Abstract summary: 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.
• Score: 2.2136680238528665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamical system. 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 paper, 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.

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