Automated P&ID Information Extraction Using Deep Learning
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Solution Overview
Problem
Current systems lack an automated mechanism for extracting and analyzing information from piping and instrumentation diagrams, which are often manually maintained as hard-copies or scanned images, leading to expensive and labor-intensive processes for inventory management and process improvement.
Innovation Solution
A system utilizing deep learning techniques and image processing to detect and associate components in piping and instrumentation diagrams, generating tree-shaped data structures to represent process flows, and employing Connectionist Text Proposal Networks and Fully Convolutional Networks for robust detection and classification of pipeline codes, inlets, outlets, and symbols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual techniques are used for information extraction from piping and instrumentation diagrams, then domain expertise can be applied for accurate interpretation, but the process becomes expensive and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with automated computer vision and deep learning systems. Specifically, it uses Fully Convolutional Networks for symbol detection, Connectionist Text Proposal Networks for text detection, and graph neural networks for relationship extraction, thereby substituting human expert labor with automated computational systems that maintain accuracy while dramatically improving productivity
Solution Approach 2:
The patent introduces intermediate processing layers including image preprocessing modules, feature extraction networks, and relationship inference modules that mediate between the input diagrams and final extracted information. These intermediaries transform the raw visual data into structured representations that can be accurately interpreted without requiring direct human expert intervention
2Reliability
If piping and instrumentation diagrams are stored as scanned images, then original documentation is preserved, but automated information extraction cannot be performed
Solution Approach 1:
The patent replaces the need for manual digitization and conversion processes with direct automated computer vision analysis of scanned images. The system processes image files directly through deep learning models to extract structured information, eliminating the need for intermediate manual transcription or conversion steps while maintaining documentation integrity
Solution Approach 2:
The patent creates digital copies of the information contained in scanned diagrams through automated detection and extraction. Instead of requiring the original diagrams to be physically digitized or manually transcribed, the system generates digital representations of the extracted information (symbols, text, relationships) directly from the scanned images, preserving the original while creating usable digital data
3Measurement precision
If traditional image processing methods are used for symbol detection, then computational resources are consumed less, but detection accuracy decreases due to noisy textual information and minute visual differences
Solution Approach 1:
The patent transforms the detection problem by changing parameters including using deep learning feature representations instead of traditional image processing features, applying data augmentation to handle noise and variations, and using hierarchical detection strategies that process information at multiple scales and levels of abstraction to improve accuracy while managing computational costs
Data Source
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AI summary
Systems and methods for automating information extraction from piping and instrumentation diagrams is provided. Traditional systems and methods do not provide for end-to-end and automated data extraction from the piping and instrumentation diagrams. The method disclosed provides for automatic generation of end-to-end information from piping and instrumentation diagrams by detecting, via one or more hardware processors, a plurality of components from one or more piping and instrumentation diagrams by implementing one or more image processing and deep learning techniques; associating, via an association module, each of the detected plurality of components by implementing a Euclidean Distance technique; and generating, based upon each of the associated plurality of components, a plurality of tree-shaped data structures by implementing a structuring technique, wherein each of the plurality of tree-shaped data structures capture a process flow of pipeline schematics corresponding to the one or more piping and instrumentation diagrams.