Machine Learning Drive Configuration With Simulation Feedback
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Solution Overview
Problem
Current drive configuration methods in industrial powertrains lack a comprehensive tool that integrates system design, simulation engineering, and expert knowledge, leading to inefficient and costly iterations and potential misconfigurations.
Innovation Solution
A system and method utilizing machine learning to collect and analyze data from various sources, generate optimal configuration parameters, and simulate these configurations to improve efficiency and reduce engineering time and costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If separate tools are used for powertrain selection, simulation, and drive configuration, then each tool can be optimized for its specific function, but the overall engineering time and complexity increase due to lack of integration
Solution Approach 1:
The patent merges separate configuration tools into a single integrated drive configuration tool that combines powertrain selection, simulation, and configuration parameters generation. This integration allows the system to maintain specialized functionality while reducing overall system complexity and improving ease of use by providing a unified interface and workflow.
2Productivity
If configuration parameters are generated based on partial knowledge without comprehensive integration, then initial setup is faster, but iterative adjustments and verification increase engineering time and costs
Solution Approach 1:
The system performs preliminary actions by gathering comprehensive application details and system requirements at the beginning of the configuration process. It then uses this complete information to generate initial configuration parameters that are more accurate and require fewer iterative adjustments, thereby reducing overall engineering time despite the thorough initial analysis.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors configuration parameters against system requirements and performance criteria. This feedback loop enables automatic detection and correction of misconfigurations, reducing the need for manual iterative adjustments and verification time.
3Ease of operation
If expert knowledge is not integrated into the configuration process, then the system is easier to operate, but configuration accuracy and reliability decrease leading to misconfigurations
Solution Approach 1:
The system incorporates expert knowledge bases and automated decision-making algorithms that enable the configuration tool to serve itself by automatically selecting appropriate parameters and making configuration decisions. This self-service capability maintains ease of operation while ensuring configuration accuracy through embedded expert rules and validation mechanisms.
Data Source
AI summary
A system for optimal drive configuration using machine learning; the system includes: a data collector configured to collect data and establish correlations among the collected data; a training data set generator configured to compute configuration sets based on the collected data and based on the established correlations, further configured to compute measured success values for the configuration sets, further configured to generate training data sets that include the configuration sets together with corresponding measured success values; a machine learning module, configured to predict predicted success values for calculated configuration sets using the training data sets provided by the training data set generator using machine learning algorithm; and an optimization module, configured to order the calculated configuration sets, including a simulation module, configured to simulate the calculated configuration sets.