Identification of Modules from Graphical Control Specification

Industrial process automation engineering is embracing a modular approach instead of monolithic process plants. In this paper we propose a tool to identify a set of possible modules from a monolithic control specification in the form of a Scientific Apparatus Makers Association (SAMA) document in order to significantly eliminate manual engineering effort. The proposed approach performs vector image processing to extract entities, equipment blocks, and the control flow from the document and store the information as a labeled property graph. Next, we derive a set of possible modules so that the process plant can be implemented as a composition of a set of modules. We perform an unsupervised, K-Means clustering approach to partition the graph where each partition becomes a potential module. Each module consists of a set of equipment blocks, IOLists, and a service description. We leverage a proprietary module design tool to import each cluster as a module skeleton and allow the developer to complete the module description. We have tested the efficacy of this approach on two large SAMA documents and verified the quality of clustering with the domain experts.