Multivariate process monitoring and analysis based on multi-scale KPLS

In the paper, a new multi-scale KPLS (MSKPLS) algorithm combining kernel partial least square (KPLS) and wavelet analysis is proposed for investigating the multi-scale nature of nonlinear process. The MSKPLS first decomposes the process measurements into separated multi-scale components using on-line wavelet transform, and then the resultant multi-scale data blocks are modeled in the framework of multi-block KPLS algorithm which can describe the global relationships across the entire scales as well as the localized features within each scale. To demonstrate the feasibility of the MSKPLS method, its process monitoring abilities were tested for a real industrial data set, and compared with the monitoring abilities of the standard KPLS method. The results clearly showed that the MSKPLS was superior to the standard KPLS, especially in that it could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability.

► A new multi-scale KPLS algorithm was proposed for monitoring processes. ► MSKPLS decomposes the process measurements into separated multi-scale components using on-line wavelet transform. ► MSKPLS resultant multi-scale data blocks are modeled in the framework of multi-block KPLS algorithm. ► MSKPLS could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability.