Recursive Slow Feature Analysis for Adaptive Monitoring of Industrial Processes

Recently, a new process monitoring and fault diagnosis method based on slow feature analysis has been developed, which enables concurrent monitoring of both operating point and process dynamics. In this paper, a recursive slow feature analysis algorithm for adaptive process monitoring is put forward to accommodate time-varying processes by updating model parameters and monitoring statistics once a new sample arrives. An important algebraic property of slow feature analysis is first established. We then show that such a property can be violated by online updating with a forgetting factor used, and a remedy is suggested. A novel algorithm based on the rank-one modification and the orthogonal iteration procedure is proposed to recursively adjust the solution to the generalized eigenvalue problem, model parameters, and associated monitoring statistics in a cost-efficient way. In addition, an improved stopping criterion for model updating is proposed based on the statistics relevant to process dynamics, which yields an intelligent maintenance mechanism of monitoring systems. The efficacy of the proposed method is finally evaluated on a real crude heating furnace system.