Fault Detection via Occupation Kernel Principal Component Analysis
This work addresses fault detection for automatic systems, presenting an incremental improvement with a data-driven approach that handles irregular data.
The paper tackled fault detection in dynamical systems by proposing a novel principal component analysis method using occupation kernels, which was validated through numerical simulations to demonstrate efficacy.
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.