Using improved self-organizing map for fault diagnosis in chemical industry process

There are numerous fault diagnosis methods studied for complex chemical process, in which the effective methods for visualization of fault diagnosis are more challenging. In order to visualize the occurrence of the fault clearly, a novel fault diagnosis method which combines self-organizing map (SOM) with correlative component analysis (CCA) is proposed. Based on the sample data, CCA can extract fault classification information as much as possible, and then based on the identified correlative components, SOM can distinguish the various types of states on the output map. Further, the output map can be employed to monitor abnormal states by visualization method of SOM. A case study of the Tennessee Eastman (TE) process is employed to illustrate the fault diagnosis and monitoring performance of the proposed method. The results show that the SOM integrated with CCA method is efficient and capable for real-time monitoring and fault diagnosis in complex chemical process.

► We focus on the intuitive visualization method for fault detection. ► The proposed fault diagnosis method combines SOM with CCA. ► CCA can extract fault classification information for SOM. ► Case study demonstrates its performance better than SOM and PCA-SOM.