Performance Assessment for Process Monitoring and Fault Detection Methods

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.ContentsAssessing the performance of T2 and Q fault detection statisticsProposing a new performance evaluation index called expected detection delay (EDD) Assessing the performance of different PM-FD methods using EDD when applied to detecting different types of faultsAssessing the state-space-based PM-FD methods when applied to a real hot strip mill processTarget GroupsScientists and students in the field of process control and statistical quality controlElectrical engineers, chemical engineers, hot strip steel mill engineersAbout the AuthorKai Zhang has just finished his PhD defense. His research area covers multivariate statistical process monitoring (PM) methods, data-driven fault detection (FD) methods and performance evaluation for PM-FD methods.