Aging-Aware Reward Modeling for Control System Drift
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Solution Overview
Problem
Existing machine learning solutions for autonomous control systems often fail to account for aging and drift in machinery, leading to inefficiencies and potential safety issues in sensitive applications, as they prioritize short-term optimization over long-term component health.
Innovation Solution
Incorporating an aging-aware machine learning agent that extracts an aging model to predict future performance degradation, allowing it to adjust control system settings to balance current and future performance, thereby prolonging equipment lifespan and reducing maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning agent optimizes for product throughput, then productivity is improved, but manufacturing precision deteriorates due to aging machinery drift
Solution Approach 1:
The patent applies preliminary action by extracting an aging model that predicts future performance degradation before it occurs. The machine learning agent uses this predictive model to anticipate machinery drift and adjust control parameters proactively, preventing precision loss while maintaining throughput optimization.
Solution Approach 2:
The patent implements feedback by continuously monitoring actual product characteristics and comparing them against predictions from the aging model. This closed-loop feedback enables the machine learning agent to dynamically adjust control parameters, correcting for aging effects in real-time and maintaining manufacturing precision alongside productivity.
2Manufacturing precision
If static margining is used to compensate for aging, then manufacturing precision is maintained, but device complexity increases due to overcompensation
Solution Approach 1:
The patent applies dynamics by replacing static margining with a dynamic, adaptive approach. The machine learning agent continuously updates control parameters based on real-time aging predictions from the extracted aging model, allowing the system to adjust precisely to actual aging rates rather than using fixed overcompensation margins.
Solution Approach 2:
The patent implements parameter changes by modifying control parameters dynamically based on aging predictions. Instead of using fixed static margins, the system adjusts parameters such as flow rates, temperatures, or pressures in response to predicted aging effects, maintaining precision while avoiding the complexity of static overcompensation designs.
3Manufacturing precision
If control parameters are adjusted to maintain precision, then manufacturing precision is improved, but productivity decreases due to conservative settings
Solution Approach 1:
The patent applies preliminary action by using the aging model to predict future precision losses before they occur. This allows the system to make targeted, minimal adjustments to control parameters only when and where needed to maintain precision, rather than applying conservative settings across the board that would reduce productivity.
Solution Approach 2:
The patent implements local quality by applying precision-maintaining adjustments only to specific control parameters affected by aging, rather than uniformly reducing all parameters. The machine learning agent identifies which parameters need adjustment based on the aging model predictions, maintaining precision in critical areas while preserving productivity in non-critical areas.
Data Source
AI summary
The techniques disclosed herein enable systems to integrate aging awareness into machine learning agents for management of control systems. To achieve this, the machine learning agent extracts a set of states and an aging model from the control system and derives an aging term from the aging model. Based on the states and the aging term, the machine learning agent determines a set of actions that are applied to the control system. Subsequently, the machine learning agent can extract a new set of states to analyze the efficacy of the set of actions. This is achieved through an optimality function that quantifies the success of the set of actions by calculating an optimality score. The calculation is based on both a current performance of the system and a future drift or degradation of system components. Through many iterations of the action set, the machine learning agent can optimize system operations.