Fault detection and isolation in transient states using principal component analysis

This paper proposes the use of principal component analysis (PCA) for process monitoring and fault detection and isolation in processes with several operation modes and long transient states and start-ups. The principal aspects of the PCA approach and the necessary transformations for dealing with this type of processes are presented. In this paper a classical PCA model is used for each steady state of the process and a modification of a batch PCA approach is applied to the transient states of the continuous process. So, in this last case, the PCA model is performed over a three way matrix arranged with the values of the measured variables of several past transitions with a nominal behaviour. This approach presents some problems, such as the unfolding, alignment and imputation. The methods proposed to deal with these problems are explained in detail and compared in order to design a fault detection and isolation method. Two examples are considered to perform the tasks explained. In both cases good results are obtained.

► We use PCA for FDI in processes with several operation modes and long transient states and start-ups.
► Classical PCA is used for fault detection in the different steady states of the process.
► We develop several modifications of batch PCA technique to detect faults in the transient states.
► We apply the developed method to a simulated sugar factory and a real plant with good results.