Fault diagnosis and process monitoring using a statistical pattern framework based on a self-organizing map
来源期刊：中南大学学报(英文版)2015年第2期
论文作者：SONG Yu(宋羽) JIANG Qing-chao(姜庆超) YAN Xue-feng(颜学峰)
文章页码：601 - 609
Key words：statistic pattern framework; self-organizing map; fault diagnosis; process monitoring
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.
SONG Yu(宋羽), JIANG Qing-chao(姜庆超), YAN Xue-feng(颜学峰)
(Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education
(East China University of Science and Technology), Shanghai 200237, China)
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.
Key words:statistic pattern framework; self-organizing map; fault diagnosis; process monitoring
