Recursive SFA Algorithm and Adaptive Monitoring System Design

Abstract

The common occurrence of slow drifts in industrial processes ask for the need of adaptive monitoring. In this chapter, a recursive slow feature analysis algorithm for adaptive process monitoring is developed to handle time-varying processes. An algebraic property of slow feature analysis is first revealed. We then show that such a property may be violated in the presence of online updating, and we give an effective remedy. A novel algorithm based on rank-one modification and orthogonal iteration procedure is developed to recursively adjust the solution to the generalized eigenvalue problem, model parameters, and associated monitoring statistics conveniently. In addition, an improved stopping principle for model updating is proposed based on statistics related to process dynamics, which provides an intelligent maintenance mechanism of monitoring systems. The efficacy of the proposed schema is finally evaluated on a industrial crude heating furnace system.