Industrial Process Model Generation With Feedback-Guided Simulation
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Solution Overview
Problem
Industrial applications of machine learning face challenges due to the scarcity of labeled training data, and high-fidelity simulations are time-consuming and computationally expensive, making it difficult to improve machine learning model performance effectively.
Innovation Solution
An industrial process model generation system that utilizes a simulator to generate behavioral data from both operational and simulated input value trajectories, implementing a machine learning algorithm to determine when to train or stop training based on performance conditions such as target accuracy and false rates, optimizing the use of high-fidelity simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If high-fidelity simulations are used to generate training data, then the quality and quantity of training data is improved, but the computational cost and time consumption increase significantly
Solution Approach 1:
The system performs partial simulations by selectively generating training data only for specific process scenarios and conditions that are most valuable for model training, rather than exhaustively simulating all possible operating conditions. This partial action approach provides sufficient training data quality while significantly reducing computational resources required
Solution Approach 2:
The system performs preliminary analysis to identify which training data scenarios will be most beneficial before executing simulations. By pre-determining the most valuable data generation tasks based on initial model performance gaps, the system avoids wasting computational resources on simulations that would provide minimal improvement
2Reliability
If high-fidelity simulations are used to generate additional training data, then machine learning model performance is improved, but the time consumption increases
Solution Approach 1:
The system generates training data selectively for the most critical and informative scenarios rather than comprehensively simulating all possible conditions. This partial data generation approach achieves sufficient model performance improvement while significantly reducing the time required for data generation
Solution Approach 2:
The system implements iterative feedback loops where model performance is continuously evaluated and simulation data generation is adjusted based on performance gaps. The system learns from previous simulation results to prioritize future data generation tasks, reducing overall time consumption while maintaining performance improvement
3Quantity of substance
If blind generation of simulation data is performed, then data quantity is increased, but the impact on machine learning algorithm performance is not guaranteed
Solution Approach 1:
The system performs preliminary analysis to identify specific scenarios, conditions, and data characteristics that will be most beneficial for model training before generating simulation data. This targeted approach ensures that generated data directly addresses model performance gaps rather than simply increasing data quantity
Solution Approach 2:
The system dynamically adjusts simulation parameters and data generation characteristics based on model performance feedback. By changing which parameters are simulated and how data is generated, the system ensures that simulation data quantity translates into actual performance improvement rather than just increasing dataset size
Data Source
AI summary
A model generation system includes input and output units. The input unit receives a plurality of input value trajectories comprising operational input value trajectories and simulation input value trajectories relating to an industrial process. The processing unit implements a simulator of the industrial process and generates behavioral data for at least some of the plurality of input value trajectories. The processing unit further implements a machine learning algorithm that models the industrial process, and trains the machine learning algorithm.