Component detection in piping and instrumentation diagrams of nuclear power plants based on neural networks

Abstract

Piping and Instrumentation Diagrams (P&IDs) are the most commonly used engineering drawings to describe components and their relationships, and they are one of the most important inputs for data analysis in Nuclear Power Plants (NPP). In traditional analysis, the information related to the components is extracted manually from the P&IDs. This usually takes large amounts of effort and is error-prone. With the rapid development in the area of computer vision and deep learning, automatically detecting components and their relationships becomes possible. In this paper, we aim to use the latest neural network models to automatically extract information on components and their identifications, from the P&IDs in NPPs. We use a Faster Regional Convolutional Neural Networks (Faster RCNN) architecture called ResNet-50, to detect the components in the P&IDs. Compared to common object detection, object detection for P&IDs poses unique challenges to these methods. For example, the P&IDs symbols are much smaller than the background, and detecting such small objects remains a challenging task for modern neural networks. To address these challenges, we 1) propose several techniques for data augmentation that effectively solve the problem of training data shortage, and 2) propose a feature grouping strategy for detecting components with distinct features. Besides, we introduce a SegLink model for text detection, which can automatically extract components’ identifications from P&IDs. We also develop a method for building a data structure to reflect the relationships between components (e.g., to which pipe a component is connected, or what are the downstream or upstream components of one specific component) based on the extracted information. This data structure can be further used for plant safety analysis, and operation and maintenance cost optimization. Sensitivity analysis and comparison with other Convolutional Neural Networks (CNNs) are performed. The results of these analyses are also discussed in this paper.This analysis framework has been tested on the P&IDs from a commercial NPP. The Average Precision for components, which is used to measure the performance of the proposed method, is about 98%. The success rates of component-text mapping and component-pipe mapping are 270/275 and 319/319, respectively. It is worth noting that this framework is generic and can also be applied to P&IDs of non-nuclear industries.