Process Monitoring Using Multiscale Methods

Principal component analysis is widely used in disturbance detection, isolation and diagnosis in industrial and chemical processes, and several extensions of the basic principal component methodology have been considered in previous chapters to handle features such as autocorrelation in data, time–frequency localization and nonlinearity. In this chapter, a statistical process control approach based on singular spectrum analysis is proposed. The method involves expressing a time series as the sum of identifiable components whose basis functions are obtained from measurements. Using decomposition by means of singular spectrum analysis, a multimodal representation is obtained that can be used together with existing statistical process control methods to construct a novel process monitoring scheme. It is observed that singular spectrum analysis can perform significantly better than other methods, particularly in detecting mean shift changes. However, the performance of the approach can degrade in the presence of parameter changes, as well as excessive autocorrelation of the variables.