Fault Detection via Occupation Kernel Principal Component Analysis

Rushikesh Kamalapurkar, Benjamin P. Russo, Zachary Morrison, Yingzhao Lian

Publication date: 20 March 2023

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.

Has companion code repository: https://github.com/rlkamalapurkar/OKPCA

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