Improved PCA with optimized sensor locations for process monitoring and fault diagnosis

Process monitoring and fault diagnosis using the principal component analysis (PCA) has been studied intensively and applied to industry processes. The emphasis of most PCA-based works has been mainly on procedures to perform monitoring and diagnosis for given a set of sensors, and little attention is paid to the actual location of sensors for efficient detection and identification of process faults. In this paper, graph-based techniques are used to optimize sensor locations to obtain the maximum fault resolution. Based on the optimized sensor network, an improved PCA is proposed by introducing two new statistics of PVR and CVR to replace the Q statistic in the conventional PCA. The improved PCA can efficiently detect weak changes, and give an insight into the root cause of process faults. Simulation results of a CSTR process show that the improved PCA with optimized sensor locations is superior to the conventional methods.