A novel fault diagnosis method based on multilayer optimized PCC-SDG

Chemical process failures are often caused by a series of variables with a chain effect. This study utilizes variable correlation characteristics, PCC (Pearson correlation coefficient) statistical index, and SDG (signed directed graph) to describe the causal relationship among variables, and then proposes a PCC-SDG fault diagnosis method based on a multi-layer optimization structure. With the topological network structure of the whole process as reference, this method first performs an initial optimization on the selected variable. An optimal PCC-SDG network is then constructed on the specific variables which have large PCA (principal component analysis) weights in the multilayer correlation coefficient set. After that, the rule of gather weighting coefficient Q is established to identify process fault. The application on Tennessee Eastman process illustrates that the PCC-SDG method can realize fault detection and isolation tasks in an effective pattern. Because its modeling and diagnosis procedures are simple and SDG can be readily probed for the root cause, the proposed method has an advantage in process supervision. © All Right Reserved.