LAUSR

In industrial processes, the quality of the product is crucial. The batch partial least squares (PLS) monitoring model can effectively monitor for quality-related faults. In process monitoring, to overcome time-varying disturbances, the monitoring model needs to be updated regularly. Efficiently updating the monitoring model represents a serious problem. This paper proposes a recursive concurrent projection to latent structures (RCPLS) algorithm, which can both update models more efficiently with historical model parameters and new data and provide better quality-related fault monitoring results than can static concurrent projection to latent structures (CPLS). Based on RCPLS, a complete set of process monitoring technologies is proposed. These technologies can automatically filter and store modellable data and adaptively update the online monitoring model. The updated computational quantities of the RCPLS model and the CPLS model are compared through the Tennessee Eastman process (TEP). The effectiveness of the RCPLS algorithm is verified, and a comprehensive comparison of the quality-related fault detection capabilities of RCPLS and CPLS is performed. The results show that RCPLS can significantly reduce the computational burden and increase the monitoring performance.