Transferable manifold projection embedded dictionary learning for multimode process monitoring

This paper proposes a novel transferable manifold projection embedded dictionary learning (TMPDL) based scheme with domain transfer for multimode process (MP) monitoring, where the new modes in evolving scenarios can be rapidly modeled. Considering that only new measurements are necessary for updating the model parameters, the proposed method elevates engineering applicability. Firstly, in order to quantitatively analyze the discrepancy between the new and previous modes, the common features are extracted by TMPDL, upon which new modes can be modeled using domain transfer, saving storage resources and ensuring scalability. Then, the corresponding optimization process is fully discussed, which incorporates feature selection and extraction to select specific features for updating while enhancing the interpretability of the model. Concurrently, consistency and independence constraints are imposed on dictionary learning, which makes the features extracted by the proposed method more discriminative. Finally, the monitoring model is developed by feature reconstruction error, which can derive monitoring results prior to mode identification. Experiments on the real hot strip mill process (HSMP) reveal that the fault detection ability of TMPDL is highly robust against MP, achieving a 94.8% monitoring accuracy rate for the newly arriving mode.