Adaptive process monitoring via multichannel EIV lattice filters

Online monitoring of multivariable processes is crucial to operational safety and product quality. For this, multivariable statistical analysis methods, such as principal‐component analysis (PCA), partial least squares, and canonical variate analysis have been widely applied. However, few recursive monitoring techniques have been developed for fully dynamic and time‐varying processes. Recursive PCA has been successfully applied to monitor static time‐varying processes, but does not work for fully dynamic processes. Dynamic PCA has been developed, but its recursive variant is not available. Many processes operate in dynamic states and are often time‐varying, and the time‐varying property includes the variation of parameters and of process structure, e.g., the change of model order. A novel approach to the adaptive monitoring of multivariate dynamic and time‐varying processes by the recursive multichannel instrumental variable (IV) lattice filters was developed using the errors‐in‐variables (EIV) state space model to represent a dynamic process. To show the relationship between EIV state‐space representation of the process and a multichannel IV lattice filter, the lattice filter was used to generate a residual vector for process monitoring. By using lattice filter's ability of recursively updating the process model both in time and order, a real time, on‐line algorithm was used to update the residual vector with newly sampled process data, including a practical approach to recursive determination of the process model order. Based on the residual vector, the Hotelling T 2 statistic and the associated confidence limits are used as the monitoring index. The proposed scheme was evaluated on a simulation example and a pilot plant to support the theoretical results.