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Fault diagnosis in discrete-event systems: incomplete models and learning
Raymond H Kwong1, David L Yonge-Mallo
1Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 3G4, Canada. kwong@control.utoronto.ca
Summary
This study introduces a learning diagnoser for fault diagnosis in discrete-event systems (DESs) with incomplete models. It simultaneously diagnoses faults and learns missing system information, improving upon traditional methods.
Area of Science:
- Control Theory
- Computer Science
- Systems Engineering
Background:
- Model-based fault diagnosis for discrete-event systems (DESs) typically requires complete and accurate system models.
- Existing models may be incomplete due to abstraction or data-driven construction, failing to capture full system dynamics.
- Incomplete models lead to discrepancies between predicted and actual system outputs, hindering accurate fault diagnosis.
Purpose of the Study:
- To address fault diagnosis challenges in DESs when system models are incomplete.
- To develop a novel approach that integrates learning into the diagnoser construction process.
- To enable simultaneous fault diagnosis and learning of missing model information.
Main Methods:
- Introduced a learning diagnoser that forms hypotheses to explain discrepancies arising from incomplete models.
- Formalized the hypothesis generation and evaluation process as an instance of the set-cover problem using parsimonious covering theory.
- Detailed the construction of the learning diagnoser, demonstrating its capability to learn missing model components.
Main Results:
- The learning diagnoser effectively performs fault diagnosis even with incomplete system models.
- The proposed method successfully learns missing information from the system's dynamic behavior.
- When the system model is complete, the learning diagnoser functions identically to standard state-based diagnosers.
Conclusions:
- Fault diagnosis in DESs can be achieved even with incomplete models by integrating learning.
- The learning diagnoser provides a unified framework for both fault identification and model refinement.
- This approach enhances the robustness and applicability of model-based diagnosis for complex systems.
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