ML Competence Monitoring for Physical Asset Control Drift
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Solution Overview
Problem
Machine learning-based physical asset control systems struggle to accommodate novel inputs and abrupt changes in data distribution, requiring frequent or prolonged data collection for retraining, which is resource-intensive and costly.
Innovation Solution
A competence module that monitors and assesses the competence of machine learning models, determining whether inputs are within a competent or incompetent region, and triggers learning mechanisms to update the model in regions of incompetence, allowing for continuous learning and self-awareness of limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If training data is collected more frequently or for a longer period to compensate for outdated data, then the machine learning model maintains better performance, but the time and resources required increase significantly
Solution Approach 1:
The system implements a feedback mechanism where the ML model monitors its own performance metrics and data distribution characteristics in real-time. When performance degradation or distribution shift is detected, the system automatically triggers data collection and retraining processes, creating a closed-loop system that optimizes the balance between model reliability and resource consumption
Solution Approach 2:
The patent transforms the static, periodic retraining approach into a dynamic system that adapts its behavior based on current conditions. The ML model continuously assesses its competence and adjusts the retraining frequency according to actual performance needs, data distribution changes, and operational context, rather than following a fixed schedule
2Reliability
If training data is collected more frequently or for a longer period to compensate for outdated data, then the machine learning model maintains better performance, but the resources required increase significantly
Solution Approach 1:
Instead of performing complete retraining of all ML models at fixed intervals, the system applies partial updates only to specific models or components that show performance degradation or are affected by data distribution shifts. This selective approach reduces computational overhead while maintaining overall system performance
Solution Approach 2:
The system dynamically changes training parameters such as learning rate, batch size, and data sampling strategies based on the current operational context and model performance state. This allows the system to optimize resource utilization during different phases of operation and adapt to varying computational requirements
3Reliability
If complete retraining of all machine learning models is performed at fixed intervals, then the system maintains consistent performance, but the complexity and cost of the system increase
Solution Approach 1:
The patent divides the monolithic retraining process into segmented, independent units. Each ML model or model component can be evaluated and updated independently based on its specific performance characteristics and data requirements. This segmentation reduces system complexity by allowing targeted updates rather than mandatory full-system retraining
Solution Approach 2:
The ML model performs self-assessment of its performance and competence, automatically determining when retraining is necessary without requiring complex external monitoring systems. The model itself generates signals for data collection and triggers its own update process, simplifying the overall system architecture while maintaining performance consistency
Data Source
AI summary
According to some embodiments a competence module is provided to: receive an objective; select a machine learning model associated with the objective; receive data from the at least one data source; determine at least one next input based on the received data; determine whether the at least one next input is in a competent region or is in an incompetent region of the machine learning model; when the at least one next input is inside the competent region, generate an output; determine an estimate of uncertainty for the generated output; when the uncertainty is below an uncertainty threshold, the machine learning model is competent and when the uncertainty is above the uncertainty threshold, the machine learning model is incompetent; and operate the physical asset based on one of the competent and incompetent state of the machine learning model. Numerous other aspects are provided.