LAUSR

Process variables obtain significant dynamic variation characteristics from the moment faults are introduced through a continuous period of time. Therefore, these data possess not only strong corresponding fault information but also robust dynamic characteristics that can be effectively used for fault diagnosis. Accordingly, the dynamic partial least squares (DPLS) and self-organizing map (SOM) combination methodology is utilized for visual fault diagnosis and monitoring. Different types of fault data (from normal to anomalous) are collected and trained by DPLS in the form of a dynamic data matrix to reveal the most discriminated orientations for the different statuses. A multilayer SOM is then trained to model the relationship with different status data under such orientations to the regular positions on a two-dimensional map. The trained map can be used for deciding the real operating state of the online observations. Excellent experimental results on the Tennessee Eastman chemical process have demonstr...