Multivariate process adjustment in the presence of fixed adjustment costs is investigated in this paper. A fixed adjustment cost leads to a trade-off between adjusting a process continuously and allowing the process to drift until a bound is violated. The problem of determining the best bounded adjustment policy and the optimal bounded adjustment parameter is addressed. Although the univariate ...

Traditionally multivariate calibration models have been developed using regression based techniques including principal component regression and partial least squares and their non-linear counterparts. This paper proposes the application of Gaussian process regression as an alternative method for the development of a calibration model. By formulating the regression problem in a probabilistic fr...

Chemical process variables are always driven by random noise and disturbances. The closed-loop control yields process measurements that are auto & cross correlated. The influence of auto & cross correlations on statistical process control (SPC) is investigated in detail. It is revealed both auto and cross correlations among the variables will cause unexpected false alarms. Dynamic PCA and ARMA-...

Process capability analysis is designed to estimate the proportion of parts that do not meet engineering requirements in a stable production process. In this paper, we review and criticize the capability indices typically used in industry for this purpose, and propose a general multivariate Bayesian capability index which contains the conventional index as a limiting case. We further derive its...

When performing process monitoring, the classical approach of multivariate statistical process control (MSPC) explicitly assumes the normal operating conditions (NOC) to be distributed normally. If this assumption is not met, usually severe out-of-control situations are missed or incontrol situations can falsely be seen as out-of-control. Combining mixture modelling with MSPC (MM-MSPC) leads to...

In this paper, an approach of wavelet-based nonlinear PCA for statistical process monitoring is presented. The strategy utilizes the optimal wavelet decomposition in such a way that only approximation and the highest detail functions are used thus simplifying the overall structure and making the interpretation at each scale more meaningful. An orthogonal nonlinear PCA procedure is incorporated ...

With process computers routinely collecting measurements on large numbers of process variables, multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance have received increasing attention. Extensions of traditional univariate Shewhart, CUSUM and EWMA control charts to multivariate quality control situations are based on Hotelling's T 2 statist...