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.
@InProceedings{jung_et_al:OASIcs.DX.2024.4, author = {Jung, Daniel and Axelsson, David}, title = {{A Study on Redundancy and Intrinsic Dimension for Data-Driven Fault Diagnosis}}, booktitle = {35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)}, pages = {4:1--4:17}, series = {Open Access Series in Informatics (OASIcs)}, ISBN = {978-3-95977-356-0}, ISSN = {2190-6807}, year = {2024}, volume = {125}, editor = {Pill, Ingo and Natan, Avraham and Wotawa, Franz}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.4}, URN = {urn:nbn:de:0030-drops-220964}, doi = {10.4230/OASIcs.DX.2024.4}, annote = {Keywords: Data-driven diagnosis, intrinsic dimension, model-based diagnosis, structural methods} }
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