Mincheol Cho’s research while affiliated with SFC Co., Ltd. and other places

Piping and instrumentation diagrams (P&IDs) are commonly used in the process industry as a transfer medium for the fundamental design of a plant and for detailed design, purchasing, procurement, construction, and commissioning decisions. The present study proposes a method for symbol and text recognition for P&ID images using deep-learning technology. Our proposed method consists of P&ID image pre-processing, symbol and text recognition, and the storage of the recognition results. We consider the recognition of symbols of different sizes and shape complexities in high-density P&ID images in a manner that is applicable to the process industry. We also standardize the training dataset structure and symbol taxonomy to optimize the developed deep neural network. A training dataset is created based on diagrams provided by a local Korean company. After training the model with this dataset, a recognition test produced relatively good results, with a precision and recall of 0.9718 and 0.9827 for symbols and 0.9386 and 0.9175 for text, respectively.

Diverse stakeholders engaged in design, construction, and operation and maintenance of offshore plants typically operate heterogeneous plant 3D CAD systems. Engineering, procurement, and construction (EPC) companies are required to submit plant design result to the owner in the form of a plant 3D CAD model, as specified in the contract. However, because of the limitations of data interface of plant 3D CAD systems, EPC companies frequently perform manual remodeling to fulfill the terms and conditions of the contract. Therefore, comparison should be performed between the source plant 3D CAD model and the remodeled plant 3D CAD model to prove the validity of the remodeled plant 3D CAD model. To automate the comparison process, we have developed a system for quantitatively assessing the similarity of the plant 3D CAD models. This paper presents the architecture and detailed functions of the system. In addition, experimental results using this system are explained.

When a plant owner requests plant 3D CAD models in the format that a shipbuilding company does not use, the shipyard manually re-models plant 3D CAD models according to the owner`s requirement. Therefore, it is important to develop a technology to compare the re-modeled plant 3D CAD models with original ones and to quantitatively evaluate similarity between two models. In the previous study, we developed a graphic user interface (GUI)-based comparison system where a user evaluates similarity between original and re-modeled plant 3D CAD models for piping design at the level of unit. However, an offshore plant consists of thousands of units and thus a system which compares several plant 3D CAD models at unit-level without human intervention is necessary. For this, we developed a new batch model comparison system which automatically evaluates similarity of several unit-level plant 3D CAD models using an extensible markup language (XML) file storing file location and name data about a set of plant 3D CAD models. This paper suggests system configuration of a batch-mode-based comparison system and discusses its core functions. For the verification of the developed system, comparison experiments for offshore plant 3D piping CAD models using the system were performed. From the experiments, we confirmed that similarities for several plant 3D CAD models at unit-level were evaluated without human intervention.