# Fault Detection via Occupation Kernel Principal Component Analysis

arXiv: https://arxiv.org/abs/2303.11138 · HTML: https://mlsanity.com/paper/2303.11138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 20 Mar 2023.

## Builds on (most cited first, 2 of 2)

- [The Occupation Kernel Method for Nonlinear System Identification](https://mlsanity.com/paper/1909.11792/) (2019)
- [Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis](https://mlsanity.com/paper/2201.02709/) (2022)

## Abstract (arXiv)

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.