Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map

Abstract

A multivariate method for fault diagnosis and process monitoring is proposed. This technique is based on a statistical pattern (SP) framework integrated with a self-organizing map (SOM). An SP-based SOM is used as a classifier to distinguish various states on the output map, which can visually monitor abnormal states. A case study of the Tennessee Eastman (TE) process is presented to demonstrate the fault diagnosis and process monitoring performance of the proposed method. Results show that the SP-based SOM method is a visual tool for real-time monitoring and fault diagnosis that can be used in complex chemical processes. Compared with other SOM-based methods, the proposed method can more efficiently monitor and diagnose faults.