1384483

11447

The emphasis of most PCA process monitoring approaches is mainly on procedures to perform fault detection and diagnosis given a set of sensors. Little attention is paid to the actual sensor locations to efficiently perform these tasks. In this paper, graph-based techniques are used to optimize sensor locations to ensure the observability of faults, as well as the fault resolution to a maximum possible extent. Meanwhile, an improved PCA that uses two new statistics of PVR and CVR to replace the Q index in conventional PCA is introduced. The improved PCA can efficiently detect weak process changes, and give an insight to the root cause about the process malfunction. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to conventional methods in fault resolution and sensibility.

735

Akaike information criterion , structure detectability , AIC , SD , common variable(s) , Singular value decomposition , MC , cumulative percentage variance , signed digraph , DG , MD , directed graph (digraph) , method detectability , PC(s) , PV(s) , Multiple correlation , CPV , SDG , principal-component-related variable(s) , CV(s) , SVD , principal component(s)

Studia Iranica

744

744