Data-based decomposition plant for decentralized monitoring schemes: a comparative study

Abstract

The complexity of the industrial processes, large-scale plants and the massive use of distributed control systems and sensors are challenges which open ways for alternative monitoring systems. The decentralized monitoring methods are one option to deal with these complex challenges. These methods are based on process decomposition, i.e., dividing the plant variables into blocks, and building statistical data models for every block to perform local monitoring. After that, the local monitoring results are integrated through a decision fusion algorithm for a global output concerning the process. However, decentralized process monitoring has to deal with a critical issue: a proper process decomposition, or block division, using only available data. Knowledge of the plant is rarely available, so data-driven approaches can help to manage this issue. Moreover, this is the first and key step to developing decentralized monitoring models and several alternative approaches are available. In this work a comparative study is carried out regarding decentralized fault monitoring methods, comparing several alternative proposals for process decomposition based on data. These methods are based on information theory, regression and clustering, and are compared in terms of their monitoring performance. When the blocks are obtained, CVA (Canonical Variate Analysis) based local dynamic monitors are set up to characterize the local process behavior, while also considering the dynamic nature of the industrial plants. Finally, the Bayesian Inference Index (BII) is implemented, based on these local monitoring, to achieve a global outcome regarding fault detection for the whole process. To further compare their performance from the application viewpoint, the Tennessee Eastman (TE) process, a well-known industrial benchmark, is used to illustrate the efficiencies of all the discussed methods. So, a systematically comparison have been carried out involving different data-driven methods for process decomposition to implement a decentralized monitoring scheme. The results are focused on providing a reference for practitioners as guidelines for successful decentralized monitoring strategies.