Course Contents

Introduction to Fault Detection and Diagnosis and its importance in Energy Technology. Applied Multivariate Statistics, Statistical Monitoring and Pattern Classification. Fault Detection and Diagnosis of engineering systems with Principal Component Analysis (PCA) Fault Detection and Diagnosis of in engineering systems with Fisher Discriminant Analysis (FDA) Fault Detection and Diagnosis of industrial processes and systems with Partial Least Squares (PLS) Fault Detection and Diagnosis in Applications Introduction to Analytical and Knowledge-Based methods for Fault Detection and Diagnosis

Course Synopsis

recognize the importance of Fault Detection and Diagnosis in reality and applications be familiar with the overall Fault Detection and Diagnosis and Process Monitoring be familiar with PCA, FDA and PLS methods for FDD

Course Learning Outcomes

be able to work with measured data and be able to extract information useful for fault detection, fault identification and fault diagnosis in industrial systems be able to detect, identify and isolate faults from data obtained from sensors of industrial processes and systems.