Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911705892651008 |
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| author | Sheikhi, Mohammad Amin Gleizer, Gabriel de Albuquerque Esfahani, Peyman Mohajerin Keviczky, Tamás |
| author_facet | Sheikhi, Mohammad Amin Gleizer, Gabriel de Albuquerque Esfahani, Peyman Mohajerin Keviczky, Tamás |
| contents | We study the problem of fault isolation in linear systems with actuator and sensor faults within a data-driven framework. We propose a nullspace-based filter that uses solely fault-free input-output data collected under process and measurement noises. By reparameterizing the problem within a behavioral framework, we achieve a direct fault isolation filter design that is independent of any explicit system model. The underlying classification problem is approached from a geometric perspective, enabling a characterization of mutual fault discernibility in terms of fundamental system properties given a noise-free setting. In addition, the provided conditions can be evaluated using only the available data. Finally, a simulation study is conducted to demonstrate the effectiveness of the proposed method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01347 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach Sheikhi, Mohammad Amin Gleizer, Gabriel de Albuquerque Esfahani, Peyman Mohajerin Keviczky, Tamás Systems and Control We study the problem of fault isolation in linear systems with actuator and sensor faults within a data-driven framework. We propose a nullspace-based filter that uses solely fault-free input-output data collected under process and measurement noises. By reparameterizing the problem within a behavioral framework, we achieve a direct fault isolation filter design that is independent of any explicit system model. The underlying classification problem is approached from a geometric perspective, enabling a characterization of mutual fault discernibility in terms of fundamental system properties given a noise-free setting. In addition, the provided conditions can be evaluated using only the available data. Finally, a simulation study is conducted to demonstrate the effectiveness of the proposed method. |
| title | Data-Driven Fault Isolation in Linear Time-Invariant Systems: A Subspace Classification Approach |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.01347 |