Control System Performance Monitoring and Diagnosis comprehensively covers various evidence based methods for control performance diagnosis. It introduces the fundamentals of Bayesian theory and its applications in general diagnosis problems, and covers existing control loop performance monitoring techniques, including a summary of the existing control performance monitors. It synthesizes observations of different monitors and suggests possible performance abnormalities using the basis of Bayesian theory.
Building on the fundamentals, a general data-driven framework for control performance diagnosis is introduced, which is followed by a number of practical engineering problems, including missing data, incomplete data, sparse data and correlated data problems and their solutions under the evidence based methods theoretical framework.
Extending beyond traditional control performance monitors with discrete outputs, this book also explores how the control loop performance monitors with continuous outputs can be directly incorporated into the evidence based methods diagnosis framework, thus improving diagnosis performance.
