Question

State space model using dynamic principal component analysis fault detection

Answer 1

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Principal Component Analysis (PCA) based, time-series analysis methods have become basic tools of every process engineer in the past few years thanks to their efficiency and solid statistical basis. However, there are two drawbacks of these methods which have to be taken into account. First, linear relationships are assumed between the process variables, and second, process dynamics are not considered. The authors presented a PCA based multivariate time-series segmentation method which addressed the first problem. The nonlinear processes were split into locally linear segments by using T 2 and Q statistics as cost functions. Based on this solution, we demonstrate how the homogeneous operation ranges and changes in process dynamics can also be detected in dynamic processes. Our approach is examined in detail on simple, theoretical processes and on the well-known pH process.