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

作　　者：宋羽 姜庆超 颜学峰 

机构地区：[1]Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education(East China University of Science and Technology)

出　　处：《Journal of Central South University》2015年第2期601-609,共9页中南大学学报（英文版）

基　　金：Project(2013CB733605)supported by the National Basic Research Program of China;Project(21176073)supported by the National Natural Science Foundation of China;Project supported by the Fundamental Research Funds for the Central Universities,China

摘　　要：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.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.

关 键 词：statistic pattern framework self-organizing map fault diagnosis process monitoring 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]