Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches

The development of large interconnected plants has brought the need for the development of active and accurate performance monitoring methods. The commonly used approach to this problem is implementing the multivariate statistics-based process monitoring (MSPM). In MSPM, data modeling methods play the central role in developing normal operation models, based on which the monitoring statistics can be used to track the process operating performance. This paper seeks to perform a comparison study on the commonly used data modeling methods in the MSPM field, including principal component analysis, partial least squares, and canonical correlation analysis, to provide users and practitioners with informative details such that useful guidance can be offered to select the preferable methods. The interconnections between each two of them are first investigated. Then, dynamic extensions based on their static formulations including how they modify and resolve objective functions are revealed. Using the simulated data from the continuous stirred tank reactor benchmark process and real industrial data from the hot strip rolling mill process, parts of theoretical results are validated.