Multiphysics Model Generation From Automation Design and Drive Data
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Solution Overview
Problem
Users in industrial automation lack the resources and technical expertise to generate process models, making it difficult to optimize industrial automation processes without access to sufficient information or expertise.
Innovation Solution
A mechanical load identification system that uses design information and drive data to generate a model of industrial automation components, including a digital twin, motion profile, and bill of materials, allowing users to improve operations without modifying physical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a user attempts to generate a process model for control optimization, then the ability to optimize process performance is improved, but the requirement for technical expertise and resources increases, which the user does not possess
Solution Approach 1:
The system creates a digital twin (virtual copy) of the physical industrial automation components and process. This digital replica can be manipulated, analyzed, and optimized without requiring the user to have deep technical expertise in the physical system. The digital model captures the essential dynamics and behavior, allowing users to perform control optimization experiments virtually.
Solution Approach 2:
The system introduces an intermediary model generation service that bridges the gap between the user and the complex process modeling requirements. This intermediary automatically generates process models from available data and component information, shielding the user from the complexity of manual model creation while still enabling process optimization capabilities.
2Measurement precision
If detailed process modeling is performed to achieve precise control, then control precision is improved, but the time and resources required for model generation increase
Solution Approach 1:
The system performs preliminary actions by automatically collecting and processing component data, drive data, and operational information to pre-generate the digital twin and process models. This preliminary model generation occurs before the user needs to perform control optimization, eliminating the time-consuming manual modeling step while maintaining high control precision.
Solution Approach 2:
The system replaces the manual, time-consuming mechanical process of model generation with an automated computational approach. By substituting automated data processing and algorithmic model generation for manual modeling efforts, the system achieves high control precision without the corresponding time investment.
3Measurement precision
If the system collects and processes extensive design information and drive data to generate accurate models, then model accuracy is improved, but the complexity of data collection and processing increases
Solution Approach 1:
The system implements a universal data processing framework that handles multiple data types (design information, drive data, operational data) through a single integrated process. This multi-functional approach consolidates what would otherwise be separate complex processing tasks into a unified system that automatically generates accurate models from diverse data sources.
Solution Approach 2:
The system performs self-service by automatically collecting, processing, and integrating data from multiple sources without requiring manual intervention. The automated data processing pipeline handles the complexity of synthesizing design information and drive data, while the user simply provides access to the data sources and receives the generated models.
Data Source
AI summary
A method includes receiving, via one or more processors, design information indicating an arrangement of a plurality of industrial automation components, wherein the plurality of industrial automation components comprises a motor. The method also includes determining, via the one or more processors, an equation of motion representing a mechanical operation of the plurality of industrial automation components based on the arrangement of the plurality of industrial automation components. Further, the method includes determining, via the one or more processors, a plurality of mechanical parameters representing the mechanical operation of the plurality of industrial automation components based on the equation of motion. Further still, the method includes generating, via the one or more processors, a model of the plurality of industrial automation components based on the plurality of mechanical parameters, wherein model represents one or more operations of a physical arrangement of the plurality of industrial automation components.