تقرير
Fault Detection via Occupation Kernel Principal Component Analysis
| العنوان: | Fault Detection via Occupation Kernel Principal Component Analysis |
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| المؤلفون: | Morrison, Zachary, Russo, Benjamin P., Lian, Yingzhao, Kamalapurkar, Rushikesh |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control, Mathematics - Optimization and Control |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/LCSYS.2023.3287568 |
| URL الوصول: | http://arxiv.org/abs/2303.11138 |
| رقم الأكسشن: | edsarx.2303.11138 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1109/LCSYS.2023.3287568 |
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