Fault diagnosis in a plant using Fisher discriminant analysis

In this paper Fisher's discriminant analysis (FDA) is used for detecting and diagnosing faults in a real plant. FDA provides an optimal lower dimensional representation in terms of discriminating between classes of data, where, in this context of fault diagnosis, each class corresponds to data collected during a specific, known fault. A discriminant function is applied to detect and diagnose faults using both simulated and real data collected from a plant: a two-tank system, showing good results.