Abstract:
This brief presents an enhanced dynamic causal digraph (EDCDG) reasoning method for fault diagnosis. In order to improve the fault isolation ability of the dynamic causal...Show MoreMetadata
Abstract:
This brief presents an enhanced dynamic causal digraph (EDCDG) reasoning method for fault diagnosis. In order to improve the fault isolation ability of the dynamic causal digraph method, a new algorithm for separating the positive and negative fault effect contributions is proposed. The proposed method was tested with an application on a three-layer board machine process. The results show that the proposed method, compared to the conventional dynamic causal digraph method, is able to detect the correct nodes, to form a better fault propagation path and to identify the responsible arcs when the system is affected by a process fault.
Published in: IEEE Transactions on Control Systems Technology ( Volume: 19, Issue: 3, May 2011)