Forward Selection Component Variable and Dynamic Inner Manifold Learning for Process Monitoring

A novel dynamic inner manifold learning algorithm based on forward selection component variables (FSV-DiML) is proposed for industrial process monitoring. The method begins with forward selection component analysis (FSCA) to select monitoring variables, effectively removing redundancies while preserving critical information. DiML is then used to construct a new objective function designed to extract dynamic latent variables with manifold structures from high-dimensional and nonlinear data. This approach enables the separation of process data into dynamic and static subspaces. However, since high-dimensional data often contains low-dimensional manifold structures, neighborhood preserving embedding (NPE) is applied to reduce the dimensionality of each subspace while preserving its manifold structure. Finally, Bayesian inference and fusion techniques are employed to merge various monitoring statistics into a unified monitoring index, further improving fault detection performance. The proposed FSV-DiML model is validated on the Tennessee Eastman and Multiphase Flow Facility processes, demonstrating significant improvements over existing methods in accurately detecting faults in complex industrial processes.