Physics-Informed Model Adaptation for Unseen Field Data
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Solution Overview
Problem
Existing models for physical systems struggle to generalize from laboratory settings to real-world environments due to differences in operating conditions, particularly in prognostics and health management, where replicating all possible failure conditions is impossible, leading to gaps in coverage and inefficient recalibration methods that are computationally expensive and unsuitable for fast-changing environments.
Innovation Solution
Implementing transfer learning and adaptation techniques, such as Jacobian Feature Regression, to augment nominal models with additive correction terms trained on perturbed-system data, allowing for both online and offline model adaptation to ensure alignment with real-world dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete retraining of machine learning models is performed to adapt to real-world environments, then model accuracy and generalization improve, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent segments the model adaptation process into two distinct phases: offline adaptation (performed during system downtime or initial deployment) and online adaptation (performed continuously during operation). This segmentation allows comprehensive model updates to be divided into batch processing and incremental learning, reducing the time penalty of complete retraining while maintaining accuracy improvements through selective updating of model components.
Solution Approach 2:
The patent implements preliminary action by performing offline adaptation in advance during system downtime or initial deployment phases. This preliminary model adaptation prepares the system for real-world conditions before full operation begins, so that when online operation starts, the model is already partially adapted and requires less intensive recalibration, thereby reducing the time loss during critical operational periods.
2Reliability
If complete retraining of machine learning models is performed to adapt to real-world environments, then model accuracy and generalization improve, but computational resources and costs increase
Solution Approach 1:
The patent segments computational workload into offline batch processing (performed when computational resources are more readily available) and online incremental updates (performed with minimal resource consumption during operation). This segmentation reduces peak computational demands and allows efficient use of resources by performing intensive adaptation tasks during offline periods when full computational power can be allocated.
Solution Approach 2:
The patent applies partial action by performing incremental online adaptation that updates only the necessary model components rather than complete retraining. This partial adaptation approach maintains model accuracy by updating critical parameters while skipping redundant computations, thereby reducing computational cost and energy consumption while still achieving the necessary adaptation to real-world conditions.
3Productivity
If models are trained exclusively on laboratory data to ensure controlled conditions, then model training efficiency improves, but generalization to real-world environments deteriorates
Solution Approach 1:
The patent merges two distinct training approaches by combining laboratory-controlled data training with real-world field data adaptation. The offline adaptation phase incorporates real-world data collected from actual system operation, merging the benefits of controlled laboratory training with the generalization advantages of real-world exposure. This combination allows the model to maintain training efficiency from laboratory data while achieving improved generalization through real-world data integration.
Solution Approach 2:
The patent uses preliminary laboratory training to establish a baseline model with good training efficiency, then performs preliminary offline adaptation using real-world data before full deployment. This preliminary action with real-world data prepares the model for generalization challenges it will face in production, bridging the gap between controlled laboratory conditions and unpredictable real-world environments before the model is fully deployed.
4Reliability
If all possible failure conditions are replicated during system operation to build comprehensive models, then model robustness improves, but system complexity and operational constraints increase
Solution Approach 1:
The patent implements self-service by allowing the system to automatically collect real-world failure and operational data during normal operation, eliminating the need for manual replication of all possible failure conditions. The system autonomously captures data from actual failures and edge cases that occur in production, using this self-collected data for offline adaptation, thereby achieving robustness without increasing testing complexity or requiring complex test setup infrastructure.
Data Source
AI summary
Systems and methods for adapting models of physical systems using transfer learning or adaptation techniques (e.g., Jacobian Feature Regression) in both online and offline modes are presented herein. The systems and methods presented herein extend implementation of transfer learning or adaptation techniques to physics-informed neural networks modeled using a state-space formulation, demonstrate that transfer learning or adaptation techniques is more sustainable than other retraining and transfer learning methods, demonstrate how an offline adaptation approach may be modified into an online adaptation technique, and demonstrate the application of online and offline adaptation algorithms on applications relevant to the oil and gas industry, such as membranes, compressors, and so forth.