Product quality and operation safety are important aspects of industrial processes, particularly those with large numbers of correlated process variables. Principal component analysis(PCA) has been widely used in multivariate process monitoring for its ability to reduce process dimensions. PCA and other statistical techniques, however, have difficulties in differentiating faults with similar time-domain process characteristics. A wavelet-based time-frequency approach is developed in this paper to improve PCA-based methods by extending the time-domain process features into time-frequency information. Subsequently, a similarity measure is presented to compare process features for on-line process monitoring and fault diagnosis. Simulation results show that the proposed multivariate time-frequency process feature is effective in both fault detection and diagnosis, illustrating the potentials for real-world application.

A PLS-based multirate dynamic modeling is proposed for quality prediction at a faster rate for multivariable processes with di6erent sampling rates between the process and quality variables. Depending on the nature of the process and quality variables, two model representations are proposed, one-block form for the process with closed-loop quality control, and two-block form for the process without the closed-loop quality control. The applications of the proposed models are compared with respect to two processes, a three-stage absorber and the TE process. The ability of the proposed model to predict quality at a faster rate is demonstrated with the complex TE process.

This paper extends the stage-based sub-PCA modeling method originally proposed by the authors to the monitoring of batch processes with durations of uneven lengths. Two models for each stage are developed, one for the stage division and the other for process monitoring. The purposes of the stage division are two-fold, to enhance process understanding and to provide stage-division information necessary for the development of PCA monitoring models. With the proposed method, batch processes with durations of uneven lengths can be effectively monitored for fault detection and diagnosis.

现代过程工业正逐渐倚重于生产小批量、多品种、高附加值产品的间歇过程。基于多元统计模型的过程监测是保障生产安全和产品质量的重要工具，从间歇过程独特的数据特性出发，将现有的多元统计建模方法进行合理的分类，简要回顾了各类方法的起源、发展及延伸的历程，除了阐述每种方法的基本原理，还详细讨论了各种方法的适用背景，相互关联及优缺点等内容，并对这一领域中依然存在的问题以及研究前景给出中肯的评述。

A stage-based quality control scheme, which combines an online adjustment strategy with the authors’ previous works on stage partial least squares(PLS) modeling and quality prediction, is developed for within-batch control of end-product quality for batch processes. Considering the inherent time-specific nature of process trajectories to the end-product quality, a critical-to-quality-control stage is introduced for quality control and stability improvement, together with guidelines on the manipulating variable selection and no-control region. The effectiveness and feasibility of the proposed scheme are illustrated on an injection molding process.

A process analysis and quality prediction scheme is proposed based on a stage-based PLS modeling for batch processes. Without any requirement of prior process knowledge, the scheme first divides a batch process into stages of different process characteristics. Subsequently, a strategy is developed to identify stages that have critical influences on concerned qualities, defined as critical-to-quality stages. Within these critical-to-quality stages, an algorithm is then further developed to identify the variables that have significant contributions to the quality variations. Finally, based on the identified nature of quality and stage relationships, a set of algorithms is developed for online quality prediction. The applications of the proposed scheme to injection molding show that the proposed analysis and quality prediction are not only effective but are also able to enhance process understanding and identify specific variables and periods for quality improvement.