2026-07-22T14:20:23Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:22096 2024-11-26T15:16:36Z ddc:004 open_access

A Study on Redundancy and Intrinsic Dimension for Data-Driven Fault Diagnosis Jung, Daniel Axelsson, David Data-driven diagnosis intrinsic dimension model-based diagnosis structural methods Data-driven fault diagnosis of technical systems use training data from nominal and faulty operation to train machine learning models to detect and classify faults. However, data-driven fault diagnosis is complicated by the fact that training data from faults is scarce. The fault diagnosis task is often treated as a standard classification problem. There is a need for methods to design fault detectors using only nominal data. In model-based diagnosis, the ability construct fault detectors depends on analytical redundancy properties. While analytical redundancy is a model property, it describes the diagnosability properties of the system. In this work, the connection between analytical redundancy and the distribution of observations from the system on low-dimensional manifolds in the observation space is studied. It is shown that the intrinsic dimension can be used to identify signal combinations that can be used for constructing residual generators. A data-driven design methodology is proposed where data-driven residual generators candidates are identified using the intrinsic dimension. The method is evaluated using two case studies: a simulated model of a two-tank system and data collected from a fuel injection system. The results demonstrate the ability to diagnose abnormal system behavior and reason about its cause based on selected signal combinations. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Daniel Jung and David Axelsson 2024 Is Part Of OASIcs, Volume 125, 35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.DX.2024.4 urn:nbn:de:0030-drops-220964 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.4 eng https://creativecommons.org/licenses/by/4.0/legalcode