Forecast Model Retraining for Autonomous Industrial Process Control
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Solution Overview
Problem
Industrial processes face challenges such as volatile operation zones, rapid changes in conditions, sensor drift, equipment degradation, and the need for scalable data processing, making it difficult to maintain optimal control without risking safety or requiring extensive human intervention.
Innovation Solution
A fault-tolerant machine learning-based control system that uses a forecast model and optimization search algorithm to automatically find optimal control parameters, allowing for virtual experiments, continuous training, and adaptation to changing conditions without human input, and handles faulty sensor data through data processing and interpolation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If actual experiments are performed on the real industrial process system to find optimal operation parameters, then the control accuracy can be improved, but the system safety deteriorates due to dangerous or impossible experiments in volatile operation zones
Solution Approach 1:
The patent creates a virtual copy of the industrial process system through a trained forecast model that replicates system dynamics. This digital twin allows exhaustive experimentation and optimization searches to be performed on the copy rather than the real system, enabling accurate determination of optimal operation parameters without exposing the actual process to dangerous conditions in volatile operation zones
Solution Approach 2:
The system performs preliminary training of the forecast model using historical data before actual control operations. This preliminary action creates a pre-validates virtual environment where optimal parameters can be determined in advance through optimization search algorithms, eliminating the need for risky real-time experiments on the actual industrial process
2Adaptability or versatility
If the forecast model is frequently retrained to adapt to fast changing operating conditions, then the adaptability is improved, but the response time deteriorates due to training computational overhead
Solution Approach 1:
The system implements autonomous retraining where the forecast model automatically detects when retraining is needed based on performance monitoring and triggering events, and self-executes the retraining process without human intervention. This self-service mechanism ensures the model adapts to changing conditions while minimizing unnecessary training operations that would waste time
Solution Approach 2:
The system continuously monitors forecast model performance and uses this feedback to determine when retraining is necessary. By establishing performance thresholds and triggering events, the feedback mechanism ensures retraining occurs only when actually needed, balancing adaptability with response time efficiency
3Manufacturing precision
If manual training and deployment of forecast model is performed, then the model accuracy can be optimized, but the operational complexity increases due to requiring continuous human input and bias
Solution Approach 1:
The system implements a complete autonomous pipeline where the forecast model is automatically trained, validated, and deployed without human intervention. Configuration files serve as the sole input, and the system self-manages the entire lifecycle including performance monitoring and retraining triggers, eliminating human bias and reducing operational complexity while maintaining accuracy through consistent automated processes
Solution Approach 2:
The system uses configuration files to parameterize and control all aspects of model training and deployment. By externalizing control parameters to configurable files rather than hard-coded procedures, the system maintains flexibility and accuracy while simplifying operations through standardized, reproducible parameter-based control
4Adaptability or versatility
If the control system processes data from hundreds or thousands of sensors across multiple plants, then the comprehensiveness of control is improved, but the data processing burden increases making scaling difficult
Solution Approach 1:
The system implements a universal forecast model architecture and autonomous training pipeline that can handle data from any number of sensors, plants, or processes. The configuration-file-driven approach and standardized data processing framework allow the same system to scale from single-sensor to multi-thousand sensor deployments without increasing operational complexity, maintaining both comprehensiveness and processing efficiency
Data Source
AI summary
The current disclosure is directed towards system and method for controlling industrial process. In one example, a method comprising deploying a forecast model for controlling an industrial process with training configurations that can be used as a single point of truth for guiding training and retraining versions of the forecast model using a model training algorithm without human input. The retraining and redeployment of the forecast model may be triggered when the performance of the forecast model degrades.