Tree based Diagnosis Enhanced with Meta Knowledge Applied to Dynamic Systems - Equipe diagnostic, supervision et COnduite
Communication Dans Un Congrès Année : 2024

Tree based Diagnosis Enhanced with Meta Knowledge Applied to Dynamic Systems

Résumé

This paper presents an online data and knowledge-based diagnosis method. It leverages decision trees where decisions are informed by diagnosis meta knowledge, specifically focusing on the properties of diagnosis indicators. This knowledge is used at each node to articulate a symbolic classification problem, outputting discriminating functions. The outcome is a multivariate decision tree that produces a compact model for diagnosis. The use of decision trees increases the explainability of the outcome, all the more so as one discovers the explicit formal expressions of diagnosis indicators, structured in the form of analytical redundancy relations. This article centers on the essential components required for the method to be suitable for dynamic systems. It is tested on the well-known two-tank system, showing that the performances match those of model-based diagnosis.
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Dates et versions

hal-04556203 , version 1 (02-05-2024)

Identifiants

  • HAL Id : hal-04556203 , version 1

Citer

Louis Goupil, Louise Travé-Massuyès, Elodie Chanthery, Thibault Kohler, Sébastien Delautier. Tree based Diagnosis Enhanced with Meta Knowledge Applied to Dynamic Systems. 12th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (2024), Best Theory Paul M. Frank paper Award, IFAC, Jun 2024, Ferrara, Italy. to appear in IFAC-PapersOnLine. ⟨hal-04556203⟩
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