Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910920663367680 |
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| author | Ghanipoor, Farhad Murguia, Carlos Trecate, Giancarlo Ferrari van de Wouw, Nathan |
| author_facet | Ghanipoor, Farhad Murguia, Carlos Trecate, Giancarlo Ferrari van de Wouw, Nathan |
| contents | Robust Recurrent Neural Networks (R-RENs) are a class of neural networks that have built-in system-theoretic robustness and incremental stability properties. In this manuscript, we leverage these properties to construct a data-driven Fault Detection and Isolation (FDI) method for sensor faults with proven performance guarantees. The underlying idea behind the scheme is to construct a bank of multiple R-RENs (acting as fault isolation filters), each with different levels of sensitivity (increased or decreased) to faults at different sensors. That is, each R-REN is designed to be specifically sensitive to faults occurring in a particular sensor and robust against faults in all the others. The latter is guaranteed using the built-in incremental stability properties of R-RENs. The proposed method is unsupervised (as it does not require labeled data from faulty sensors) and data-driven (because it exploits available fault-free input-output system trajectories and does not rely on dynamic models of the system under study). Numerical simulations on a roll-plane model of a vehicle demonstrate the effectiveness and practical applicability of the proposed methodology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks Ghanipoor, Farhad Murguia, Carlos Trecate, Giancarlo Ferrari van de Wouw, Nathan Systems and Control Robust Recurrent Neural Networks (R-RENs) are a class of neural networks that have built-in system-theoretic robustness and incremental stability properties. In this manuscript, we leverage these properties to construct a data-driven Fault Detection and Isolation (FDI) method for sensor faults with proven performance guarantees. The underlying idea behind the scheme is to construct a bank of multiple R-RENs (acting as fault isolation filters), each with different levels of sensitivity (increased or decreased) to faults at different sensors. That is, each R-REN is designed to be specifically sensitive to faults occurring in a particular sensor and robust against faults in all the others. The latter is guaranteed using the built-in incremental stability properties of R-RENs. The proposed method is unsupervised (as it does not require labeled data from faulty sensors) and data-driven (because it exploits available fault-free input-output system trajectories and does not rely on dynamic models of the system under study). Numerical simulations on a roll-plane model of a vehicle demonstrate the effectiveness and practical applicability of the proposed methodology. |
| title | Data-Driven Sensor Fault Diagnosis with Proven Guarantees using Incrementally Stable Recurrent Neural Networks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2504.19688 |