AI Model Management With Human Verification for Fault Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems require shutdown for component repair due to fault detection, leading to unexpected downtime and increased costs, and current fault detection mechanisms suffer from high false positive and false negative rates.
Innovation Solution
An AI model management system that applies AI models to input data to detect faults proactively, reducing false positives and negatives by integrating Human-In-The-Loop supervision and continuous learning to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fault detection mechanisms are implemented in large systems, then system reliability is improved, but false positives and negatives increase leading to unexpected shutdowns
Solution Approach 1:
The patent introduces human operators as intermediaries between the AI model and final fault detection decisions. The AI model provides preliminary fault classifications, but human operators verify these classifications before triggering system shutdowns. This intermediary layer reduces false positives and negatives by adding human judgment to the automated detection process, thereby improving measurement precision while maintaining system reliability.
Solution Approach 2:
The patent implements a feedback loop where human operator verifications of AI model classifications are fed back into the system. When human operators correct or confirm AI predictions, this feedback is used to continuously improve the AI model's accuracy. This feedback mechanism reduces false positives and negatives over time, enhancing both reliability and measurement precision.
2Measurement precision
If human review of component faults is implemented, then false positives and negatives are reduced, but system downtime increases due to manual verification requirements
Solution Approach 1:
The patent applies partial action by having human operators review only a subset of AI model classifications rather than all predictions. The system prioritizes classifications based on confidence scores, severity levels, and operational context, directing human review resources to the most critical or uncertain cases. This partial review approach maintains high measurement precision for critical faults while minimizing overall system downtime.
Solution Approach 2:
The patent implements preliminary action by having the AI model perform initial fault classification and filtering before human review. The AI model pre-processes large volumes of component data, identifies potential faults, and prioritizes them based on predicted severity and confidence levels. This preliminary filtering reduces the volume of data requiring human review, thereby maintaining high detection accuracy while reducing the time loss associated with manual verification.
3Productivity
If AI model is used for automated fault detection, then productivity is improved, but false positives and negatives increase
Solution Approach 1:
The patent introduces human operators as intermediaries between the AI model and final fault detection decisions. The AI model provides preliminary fault classifications at high speed, but human operators verify these classifications before triggering system shutdowns. This intermediary layer reduces false positives and negatives by adding human judgment to the automated detection process, thereby improving measurement precision while maintaining the productivity gains from automated initial screening.
Solution Approach 2:
The patent implements a feedback loop where human operator verifications of AI model classifications are fed back into the system. When human operators correct or confirm AI predictions, this feedback is used to continuously improve the AI model's accuracy. This feedback mechanism reduces false positives and negatives over time, enhancing both reliability and measurement precision while preserving the high productivity of automated detection.
4Reliability
If comprehensive fault detection is implemented across millions of components, then system reliability is improved, but operational costs increase due to manual review requirements
Solution Approach 1:
The patent applies partial action by having human operators review only a subset of AI model classifications rather than all predictions across millions of components. The system prioritizes classifications based on confidence scores, severity levels, and operational context, directing human review resources to the most critical or uncertain cases. This partial review approach maintains high system reliability while significantly reducing the operational costs associated with comprehensive manual verification.
Solution Approach 2:
The patent implements preliminary action by having the AI model perform initial fault classification and filtering before human review. The AI model pre-processes large volumes of component data from millions of system components, identifies potential faults, and prioritizes them based on predicted severity and confidence levels. This preliminary filtering enables comprehensive fault detection across the entire system while reducing the volume of data requiring expensive human review, thereby improving reliability while controlling operational costs.
Data Source
AI summary
An artificial intelligence (AI) model management system is disclosed. The system may receive data, such as image data, as part of a task to be completed. The system may apply an AI model, such as a machine learning algorithm, to the data. The system may also generate performance data based on performance of the AI model, and determine whether to route AI model results for verification. During verification, one or more inputs are received indicating whether the AI model results are correct and, if not correct, a correct result. The system may determine which inputs indicate a correct result if, for example, the inputs do not indicate the same correct result. The AI model may be trained in real-time based on the verified or corrected results. In some examples, the system causes one or more actions to be taken upon completion of the task.