Facility Asset Document Extraction for Accurate Fault Analysis
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Solution Overview
Problem
Existing methods for analyzing engineering diagrams in facilities, such as PFDs and P&IDs, are prone to errors and inefficiencies due to their complexity, leading to incorrect fault analysis and potential downtime.
Innovation Solution
An AI and ML-based system processes engineering documents using image and natural language processing to extract instrument tags and text data, validates the data, and configures data templates for accurate asset modeling and fault analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of engineering diagrams is performed, then experts can understand upstream/downstream relationships and perform root cause analysis, but the process is time-consuming and prone to errors due to diagram complexity
Solution Approach 1:
The patent replaces manual mechanical analysis of engineering diagrams with an automated image processing system. The system uses image processing techniques to detect, recognize, and extract data from diagrams automatically, eliminating the need for manual expert analysis while maintaining accuracy in understanding upstream/downstream relationships and performing root cause analysis.
Solution Approach 2:
The system enables self-service by automatically processing engineering diagrams without requiring expert intervention. The image processing system independently performs tasks such as detecting graphical elements, recognizing text, extracting data, and establishing relationships between components, allowing the system to serve itself rather than requiring manual expert analysis.
2Measurement precision
If multiple processing techniques are applied to extract data from documents, then data extraction accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the data extraction process into multiple independent processing techniques, each handling specific aspects of the document. The system segments the extraction task into: (1) image processing for detecting graphical elements and relationships, (2) optical character recognition for text extraction, and (3) data consolidation for integrating results. This modular approach improves accuracy while managing complexity through structured organization.
Solution Approach 2:
The system implements universality by creating a multi-functional processing framework that can handle various document types and extraction tasks through a unified architecture. The same system performs multiple functions including image processing, text recognition, data extraction, validation, and template configuration, reducing overall system complexity compared to having separate specialized systems for each function.
3Adaptability or versatility
If engineering diagrams follow non-standard conventions, then flexibility in representing assets is improved, but ease of automated analysis deteriorates
Solution Approach 1:
The patent applies parameter changes by enabling the system to adapt to different diagram conventions through configurable processing parameters. The system can be adjusted to recognize various graphical element representations, connection types, and text formatting styles by modifying detection and recognition parameters, allowing it to handle non-standard conventions while maintaining automated analysis capability.
Data Source
AI summary
Various embodiments described herein relate to systems and methods for extracting data from documents associated with assets in a facility. In this regard, the documents are retrieved from data sources initially. Then, a document is processed using a first processing technique to extract first data from the document. This first data corresponds to instrument tags associated with corresponding assets. The first data is validated using certain validation techniques. The document is also processed using a second processing technique to extract second data from the document such that the second data is different from the first data. The validated first data and the extracted second data are then consolidated to configure data templates associated with related assets in the facility. The data templates are also rendered on a display as well.