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 detecting different types of faults -- Assessing the state-space-based PM-FD methods when applied to a real hot strip mill process.

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 detecting different types of faults Assessing the state-space-based PM-FD methods when applied to a real hot strip mill process Target Groups Scientists and students in the field of process control and statistical quality control Electrical engineers, chemical engineers, hot strip steel mill engineers About the Author Kai 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.