Industrial AI Drift Mitigation Through Adaptive Data Sampling
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Solution Overview
Problem
Industrial plants experience data drift due to changes in operating conditions, leading to degradation of AI and ML models, which can result in poor product quality, equipment failures, and plant shutdowns, and conventional data sampling methods may lead to inadequate or negative training.
Innovation Solution
A system and method that monitors process parameters, detects drift, determines drift and process contexts, and employs a sampling strategy using a first AI model to train a second AI model for mitigating data drift, enabling effective control of industrial processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional data sampling is performed to mitigate drift, then AI model degradation due to drift can be reduced, but inadequate or negative training may occur leading to poor model performance
Solution Approach 1:
The system implements a feedback mechanism where the first AI model continuously monitors process parameters and detects drift, then uses this detection to dynamically adjust data sampling strategies. The drift detection output feeds into the sampling strategy selection, which in turn guides the second AI model's training data collection, creating a closed-loop system that adapts to changing conditions and prevents both inadequate and negative training scenarios.
Solution Approach 2:
The invention changes the parameter of data sampling from static/conventional to dynamic/adaptive by introducing drift context and process context as determining factors. The sampling strategy is no longer fixed but varies based on detected drift characteristics and current process conditions, allowing the system to optimize training data selection in real-time according to actual plant conditions.
2Measurement precision
If AI models are re-trained frequently to adapt to drift, then model accuracy can be maintained, but productivity and operational efficiency decrease due to repeated shutdowns
Solution Approach 1:
The system performs preliminary drift detection and context analysis using the first AI model before the drift significantly impacts the second AI model's performance. By detecting drift early and determining appropriate sampling strategies in advance, the system can proactively adjust training data collection and model updates, preventing accuracy degradation while minimizing disruptions to plant operations.
Solution Approach 2:
The invention introduces dynamic adaptability to the model training process by making the sampling strategy flexible and responsive to real-time drift detection. Instead of rigid periodic re-training, the system dynamically adjusts when and how to sample data for training based on actual drift conditions, allowing continuous operation while maintaining model accuracy through adaptive, on-demand training updates.
3Adaptability or versatility
If comprehensive data collection is performed to capture all drift scenarios, then model robustness improves, but data processing complexity and computational resources increase
Solution Approach 1:
The system extracts only the essential and relevant features for drift detection and context determination using the first AI model, rather than processing all available data comprehensively. By identifying and focusing on key drift indicators and contextual parameters, the system achieves robust drift adaptation while minimizing computational overhead and processing complexity, discarding unnecessary data that does not contribute to drift mitigation.
Data Source
AI summary
A system and method for mitigating data drift in an industrial plant includes monitoring, by a processor, one or more process parameters associated with an industrial plant; detecting, by the processor, a drift in one or more process parameters based on a deviation from one or more predefined process parameters; determining, by the processor, one or more drift context and process context based on drift and one or more process parameters; determining, by the processor, sampling strategy from plurality of sampling strategies based on one or more drift and process context for sampling one or more process parameters using first Artificial Intelligence (AI) model; and training, by the processor, a second AI model based on sampling strategy for mitigating data drift.