Fault Detection for Dynamic Processes Based on Recursive Innovational Component Statistical Analysis

Fault detection has long been a hot research issue for industry. Many common algorithms such as principal component analysis, recursive transformed component statistical analysis and moments-based robust principal component analysis can deal with static processes only, whereas most industrial processes are dynamic. Therefore, dynamic principal component analysis and recursive dynamic transformed component statistical analysis have been proposed to deal with dynamic processes by expanding the dimensions. The computational complexity of these algorithms are greatly increased, and these algorithms cannot divide the data space accurately. In this paper, we propose a novel algorithm called recursive innovational component statistical analysis (RICSA), which estimates the dynamic structure of the data, accurately divides the data space into dynamic components and innovational components. In unsteady state process, the statistical characteristics of data will change, and RICSA can classify these characteristics into dynamic components by dividing the data space, instead of treating them as faults, thereby reducing the false alarm rate. Through a series of comparative experiments, especially on practical coal pulverizing system in the 1000-MW ultra-supercritical thermal power plant, Zhoushan Power Plant, we found the recursive innovational component statistical analysis to realize a higher accuracy rate and a lower false alarm rate and detection delay, which verifies its superiority. We also discuss the reduced computational complexity associated with the recursive innovational component statistical analysis.