Cloud Digital Twin Modeling for Control System Test Scenarios
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Solution Overview
Problem
Industrial automation systems are complex and disparate, leading to long development cycles for control system designs due to limited and complicated testing and prediction methods before deployment.
Innovation Solution
A cloud-based digital twin modeling system that simulates and tests control system designs using hosted applications, allowing users to build a digital model, define test scenarios, and generate predicted performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If control system designs are developed and tested using current methods, then the system can be deployed, but the development cycles are long due to limited and complicated testing workflows
Solution Approach 1:
The patent creates a digital twin - a virtual copy of the physical industrial automation system - that replicates the system's behavior, structure, and operations. This digital replica enables testing and validation in the virtual environment before deploying to the physical system, eliminating the need for complex physical testing workflows and accelerating development cycles.
Solution Approach 2:
The system performs testing, validation, and performance analysis in the digital twin environment before actual deployment to the physical system. This preliminary action in the virtual space allows developers to identify and fix issues beforehand, reducing iteration cycles and speeding up the overall development process.
2Reliability
If comprehensive testing is performed on physical industrial automation systems, then system reliability is improved, but the testing process becomes time-consuming and complex
Solution Approach 1:
By creating a faithful digital replica of the physical system, the patent enables comprehensive testing to be performed on the copy rather than the original. This allows exhaustive reliability testing, edge case validation, and stress testing to be conducted in the digital twin without consuming physical system time or resources.
Solution Approach 2:
The system performs preliminary validation and testing in the digital twin environment, identifying potential reliability issues before physical deployment. This advance testing in the virtual space ensures system reliability is verified without requiring extensive physical testing time.
3Adaptability or versatility
If multiple applications are integrated for control system development and testing, then functionality is enhanced, but system complexity increases
Solution Approach 1:
The patent merges multiple separate applications and tools into a single integrated digital twin platform. This consolidation combines modeling, simulation, testing, and analysis functionalities into one unified system, enhancing versatility while reducing the complexity of managing multiple separate tools and their integrations.
Solution Approach 2:
The digital twin platform is designed as a universal system that can handle various control system development tasks - from modeling and simulation to testing and validation - within a single environment. This multi-functionality provides enhanced capabilities without the complexity of multiple specialized systems.
Data Source
AI summary
A cloud-based digital twin modeling and testing system is capable of hosting multiple applications that can be used to simulate and test versions of a control design prior to deployment, including but not limited to control programming applications, controller emulation applications, machine simulation applications, or other such platforms. The digital twin modeling environment renders an interface display that allows a user to easily build a testable digital model of a control system by connecting components of a proposed control project to these applications. The system also allows the user to define inputs and test scenarios to be executed as a simulation. Based on the connections, inputs, and test scenarios defined by the user, the modeling system leverages the hosted applications as needed to execute the defined test scenarios and generate predicted metrics for the proposed control system design.