Fault Detection and process monitoring of industrial process based on spherical kernel T-PLS

In this paper, a data-driven approach of spherical kernel total projection to latent structures (ST-KPLS) is proposed to monitor the industrial process and detect the faults in the process. Considering that T-KPLS cannot deal with the outliers occurring in the process and outliers can be taken out effectively by using spherical method, a new method called ST-KPLS is obtained by importing the idea of spherical method into T-KPLS. First of all, we elaborate the key idea of spherical method and describe the whole procedures. Then, the implement of ST-KPLS algorithm is illustrated in details. After that, we select proper data variables and apply the benchmark simulation model no. 1 (BSM1) to collect a large amount of training and testing data needed to be monitored in the process. Finally, we utilize ST-KPLS to achieve the off-line modeling and the on-line process monitoring and properly select outliers that are used to the simulation of ST-KPLS. The results of ST-KPLS are compared with those of T-KPLS to illustrate the effectiveness of the proposed approach.