Failure Knowledge Extraction for Industrial Plant Deviation Scenarios
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Solution Overview
Problem
Current systems for extracting failure knowledge in industrial plants are manually driven, time-consuming, and prone to human error, as they require curation of knowledge from diverse sources like FMEA, HAZOP, and PID documents, leading to potential loss of information and inefficiencies.
Innovation Solution
A processor-implemented method and system that automatically extracts and collates failure knowledge using domain rules and ontology extraction algorithms, linking failure scenarios with process and equipment knowledge to create a failure scenario knowledge repository, reducing manual curation time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual curation of knowledge from diverse sources is performed, then knowledge can be extracted and organized, but time consumption increases and human errors occur
Solution Approach 1:
The patent replaces the manual mechanical process of knowledge curation with an automated computer-based system that uses natural language processing, machine learning models, and automated extraction algorithms to process documents, diagrams, and reports, thereby eliminating human time consumption and reducing human errors while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service automated knowledge extraction where the computer system autonomously processes diverse information sources, extracts relevant knowledge, validates consistency, and organizes results without requiring manual human intervention at each step, thus resolving the time consumption issue while preserving reliability through automated validation mechanisms
2Extent of automation
If manual transformation of deviation-related knowledge into computer interpretable form is performed, then knowledge can be made actionable, but time consumption increases
Solution Approach 1:
The patent substitutes the manual transformation process with automated computer-based natural language processing and machine learning models that automatically convert unstructured knowledge from documents and diagrams into structured, computer-interpretable formats, eliminating time consumption while achieving complete automation
Solution Approach 2:
The system performs preliminary automated processing of diverse information sources by pre-extracting and structuring knowledge before it is needed for analysis, so that when operators require actionable insights, the knowledge is already transformed and ready for immediate use, thus eliminating time consumption
3Loss of information
If knowledge is spread across various document sources, then comprehensive failure analysis is possible, but information loss occurs during manual curation
Solution Approach 1:
The patent implements a universal automated processing system that can handle multiple types of information sources (documents, diagrams, reports) using the same NLP and machine learning frameworks, thereby managing the complexity of diverse sources through a single multi-functional platform while preserving all information from each source type
Solution Approach 2:
The system replaces manual knowledge curation with automated computer-based processing that systematically extracts and preserves information from all diverse sources without human intervention, eliminating information loss that occurs during manual transcription while using standardized algorithms to manage the complexity of handling different source types
4Productivity
If automated curation system is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent segments the automated knowledge extraction system into distinct functional modules including document processing, diagram analysis, natural language processing, machine learning model inference, and result validation, allowing each component to be optimized independently for processing speed while managing overall system complexity through modular architecture
Solution Approach 2:
The system uses automated computer-based processing with machine learning models and algorithms to replace manual operations, achieving high processing speeds through computational efficiency while managing complexity through standardized software architectures and automated validation mechanisms
Data Source
AI summary
Failure analysis of industrial plants are stored in various types of documents associated with industrial plant. The documents are used by operators of plant to address any deviation that is active in plant. The operator generally has prior knowledge of relevant processes, equipment and sensors described in a deviation scenario in these documents. However, a system that is envisaged to aid operator in real-time does not have this information readily available as this knowledge is spread across documents. Currently available systems manually curate failure knowledge thereby making the process time consuming and prone to human errors. Present disclosure provides method and system for performing extracting and collating failure knowledge from diverse sources in industrial plant. The system automatically extracts failure knowledge present in text documents using trained models and links it with process, equipment, sensor relationships that are present in piping and instrumentation diagram to create failure scenario knowledge repository.