Interactive AI Explanations for Piping Diagram Parameter Analysis
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Solution Overview
Problem
Current explanations in industrial process systems are static, making it difficult for users to understand the underlying factors and potentially leading to misunderstandings and negative perceptions. Users are unable to interact with or modify these explanations, which can result in low perceived quality and accuracy.
Innovation Solution
A method and system for interactive explanations in industrial AI systems, where users can explore explanations through cause-and-effect diagrams, historical time-series data, and simulation tools. Users can add or remove parameters, simulate alternative scenarios, and annotate user-provided data for integration into the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static explanations are provided in industrial AI systems, then system transparency is increased, but user understanding and utilization of explanations deteriorate
Solution Approach 1:
The explanation system transitions from static to dynamic by allowing users to interact with explanations through multiple formats (text, visualizations, cause-and-effect diagrams). Users can explore explanations at different levels of detail and modify parameters to see alternative scenarios, making the explanation system adaptable to user needs while maintaining transparency.
Solution Approach 2:
The patent adds interactive dimensions to explanations by incorporating temporal exploration (historical time-series data), parameter manipulation, and alternative scenario simulation. This transforms explanations from flat static text into multi-dimensional interactive experiences that include spatial visualizations and temporal comparisons.
2Device complexity
If static explanations are provided, then information presentation is simplified, but user engagement and exploration capability deteriorate
Solution Approach 1:
The explanation system serves multiple functions within a unified interface: it provides static explanations, interactive explorations, historical data comparisons, parameter manipulations, and alternative scenario simulations. This multi-functional approach maintains simplicity while enabling extensive user exploration capabilities.
Solution Approach 2:
Users can independently explore explanations by modifying parameters, comparing historical data, and generating alternative scenarios without requiring system administrator intervention. The system empowers users to self-serve their information needs through intuitive interactive features.
3Device complexity
If users cannot interact with explanations, then system simplicity is maintained, but explanation quality perception and accuracy deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where users can provide responses to explanations, annotate alternative scenarios, and modify parameters. This feedback loop allows users to validate explanations against their domain knowledge and experience, improving perceived accuracy while maintaining system simplicity through intuitive interaction patterns.
Data Source
AI summary
A method for interactive explanations in industrial artificial intelligence systems includes providing a machine learning model and a set of test data, a set of training data and a set of historical data simulating a piping and process equipment; predicting a result for the piping and process equipment based on the machine learning model using the set of test data and the set of training data, wherein the set of historical data is used by the machine learning model to predict at least one parameter of the piping and process equipment; and presenting the predicted at least one parameter on a piping and instrumentation diagram of the piping and process equipment.