LAUSR

Principal component analysis (PCA) and its variants have been widely used for process monitoring and quality control. A key issue of PCA-related methods is to select an appropriate number of principal components (PCs). However, few approaches for component selection consider the monitoring performance, and they usually rely on prior fault information. This article develops an effective algorithm for dynamic component selection, which selects components for each sample based on a detection performance index, without need for prior fault information. Component selection is transformed into a stochastic optimization problem, whose optimal solution is derived analytically. Then, a subset of components that are sensitive to faults is obtained. The proposed method reduces the requirement for the detectable fault amplitude, which leads to better performance. Furthermore, the differences between PCA and the proposed method are discussed, and online computational complexity is analyzed. Case studies on a continuous stirred tank heater (CSTH), the Tennessee Eastman process (TEP), and a real ultra-supercritical power plant demonstrate that the proposed method has better monitoring performance than PCA and some of its variants.