Variable contribution identification and visualization in multivariate statistical process monitoring

Multivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility. •Principal Component Analysis and Biplots are employed together in an innovative way to develop an efficient monitoring methodology.•An efficient and practical variable contribution identification and visualization methodology was developed and demonstrated.•Multivariate process monitoring must be employed in industry to ensure that processes perform as close as possible to optimal operation.•The methodology developed was motivated by the need for online performance monitoring considering many variables across multiple processes.