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With the growing complexity of the modern industrial process, monitoring large-scale plant-wide processes has become quite popular. Unlike traditional processes, the measured data in the plant-wide process pose great challenges to information capture, data management, and storage. More importantly, it is difficult to efficiently interpret the information hidden within those data. In this paper, the road map of a distributed modeling framework for plant-wide process monitoring is introduced. Based on this framework, the whole plant-wide process is decomposed into different blocks, and statistical data models are constructed in those blocks. For online monitoring, the results obtained from different blocks are integrated through the decision fusion algorithm. A detailed case study is carried out for performance evaluation of the plant-wide monitoring method. Research challenges and perspectives are discussed and highlighted for future work.
Ge et al. (Thu,) studied this question.