Plant Logbook Analysis Using Entity Hierarchy and AI Validation
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Solution Overview
Problem
Manual review of plant logbooks is error-prone and can lead to missing crucial information, necessitating a real-time and efficient solution for logbook analysis.
Innovation Solution
A system utilizing a tuned neural network language model for logbook analysis, incorporating entity hierarchy flow, generative AI validation, and rule-based validations to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review of logbooks is performed, then human judgment and contextual understanding can be applied, but errors occur and crucial information may be missed
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated neural network language model system. The model processes logbook entries automatically, extracting relevant information without human intervention, thereby eliminating human errors while maintaining high accuracy through sophisticated NLP techniques.
Solution Approach 2:
The system enables self-service logbook analysis where the neural network model independently processes and analyzes logbook data without requiring manual review. The automated system serves itself by continuously learning from data and improving its analysis capabilities over time.
2Loss of information
If manual review of hundreds of logbooks is performed, then comprehensive coverage is achieved, but time consumption increases significantly
Solution Approach 1:
The patent substitutes manual review with an automated neural network system that can process hundreds of logbooks simultaneously and instantaneously. This eliminates the time bottleneck while ensuring comprehensive information extraction through the model's ability to analyze all entries without omission.
Solution Approach 2:
The system performs preliminary analysis of all logbook data automatically before any human review is needed. By pre-processing and extracting key information from all hundreds of logbooks simultaneously, it prepares comprehensive results ready for verification, significantly reducing the time required for complete analysis.
3Productivity
If automated neural network analysis is implemented, then processing speed and consistency improve, but system complexity increases
Solution Approach 1:
The patent employs a universal neural network language model that can handle multiple types of logbook analyses through a single system architecture. The model is designed to process various formats and types of operational data uniformly, reducing the need for multiple specialized systems and thereby managing complexity while maintaining high productivity.
Solution Approach 2:
The system manages complexity by dynamically adjusting parameters such as attention mechanisms, processing depth, and model configuration based on the specific analysis requirements. This allows the same neural network architecture to adapt to different logbook types and analysis needs without requiring fundamentally different systems for each case.
4Reliability
If automated analysis is used, then consistency in review quality is improved, but adaptability to new logbook formats decreases
Solution Approach 1:
The patent implements a dynamic neural network system that can adapt its processing parameters and attention mechanisms based on the input logbook format. The model dynamically adjusts its analysis approach to accommodate different formats while maintaining consistent quality standards through its learned understanding of various data structures and patterns.
Solution Approach 2:
The system handles adaptability by changing its internal parameters and processing configurations based on the detected logbook format. This allows consistent analysis quality across different formats without requiring manual reconfiguration, as the model automatically adjusts its parameters to suit the specific input type.
Data Source
AI summary
A system for industrial plant logbook analysis by neural network language model, having a processor, a memory, and one or more programs stored in the memory. The one or more programs comprising instructions configured to receive a logbook of the industrial plant and extract an entity hierarchy flow providing details of hierarchy of various components of the industrial plant, such that the entity hierarchy flow is based on one or more data driven algorithms, design documentation, and a plant context hierarchy document. The system further trains the neural network language model with the entity hierarchy flow, where the training is based on a pretrained language model. The system further receives a user input requesting the industrial plant logbook analysis, such that based on the user input the system calculates a token output of the industrial plant logbook analysis using the trained neural network language model. The system further validates the calculated token output by a generative AI validation layer, updates the token output of the logbook analysis, and displays the updated output of the logbook analysis to the user.