Multiple faults diagnosis using causal graph
For engineers developing supervisory systems, this work offers a causal reasoning approach to fault diagnosis that integrates explanatory features, though it is an incremental application of existing techniques.
The paper proposes a causal graph-based diagnostic tool for multiple faults, using bond graph modeling to capture cause-effect relationships, and demonstrates improved performance over classic AI (DX) and control theory (FDI) methods in experiments.
This work proposes to put up a tool for diagnosing multi faults based on model using techniques of detection and localization inspired from the community of artificial intelligence and that of automatic. The diagnostic procedure to be integrated into the supervisory system must therefore be provided with explanatory features. Techniques based on causal reasoning are a pertinent approach for this purpose. Bond graph modeling is used to describe the cause effect relationship between process variables. Experimental results are presented and discussed in order to compare performance of causal graph technique and classic methods inspired from artificial intelligence (DX) and control theory (FDI).