Readings in model-based diagnosis
Readings in model-based diagnosis
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Intelligent Supervisory Control: A Qualitative Bond Graph Reasoning Approach
Intelligent Supervisory Control: A Qualitative Bond Graph Reasoning Approach
System Dynamics: Modeling and Simulation of Mechatronic Systems
System Dynamics: Modeling and Simulation of Mechatronic Systems
A Universal Fault Diagnostic Expert System Based on Bayesian Network
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy reasoning spiking neural P system for fault diagnosis
Information Sciences: an International Journal
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Model-based fault diagnosis using artificial intelligence techniques often deals with uncertain knowledge and incomplete information. Probability reasoning is a method to deal with uncertain or incomplete information, and Bayesian network is a tool that brings it into the real world application. A novel approach for constructing the Bayesian network structure on the basis of a bond graph model is proposed. Specification of prior and conditional probability distributions (CPDs) for the Bayesian network can be completed by expert knowledge and learning from historical data. The resulting Bayesian network is then applied for diagnosing faulty components from physical systems. The performance of the proposed fault diagnosis scheme based on bond graph derived Bayesian network is demonstrated through simulation studies.