Performance Assessment for Process Monitoring and Fault Detection Methods

Beschreibung

The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.
Contents

• Assessing the performance of T2 and Q fault detection statistics
  
• Proposing a new performance evaluation index called expected detection delay (EDD) 
  
• Assessing the performance of different PM-FD methods using EDD when applied to detectingdifferent types of faults
  
• Assessing the state-space-based PM-FD methods when applied to a real hot strip mill process
  

• Scientists and students in the field of process control and statistical quality control
  
• Electrical engineers, chemical engineers, hot strip steel mill engineers
  

The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.

Autor*in

Kai Zhang

Themen in »Performance Assessment for Process Monitoring and Fault Detection Methods«

Fault diagnosis Multivariate statistical process monitoring Data-driven Partial least squares Performance evaluation

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