Discovery of fully efficient fault indicators along a data-based diagnosis process
The integration of model-based and data-driven paradigms provides a powerful framework for fault diagnosis by combining the interpretability of analytical redundancy relations, i.e., input-output relations that are used as diagnosis indicators in model-based diagnosis, with the adaptability of learning techniques. DT4X is a recent diagnosis algorithm that uses symbolic regression to generate multivariate relations leveraging some properties of analytical redundancy relations and uses them as split functions in a decision tree. However, its symbolic regression procedure optimizes only the separation between two selected classes at each node, often fragmenting the remaining classes and degr...
| Authors | Igor Bezmaternykh, Louise Travé-Massuyès, Elodie Chanthery |
|---|---|
| Primary category | cs.AI |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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