Hybrid Device Assembly Configuration for Downtime Minimization
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Solution Overview
Problem
The challenge in manufacturing processes is to minimize downtime and optimize device configuration for automated and digitalized systems, particularly in edge computing environments where latency and real-time requirements complicate the deployment of physical and virtual devices across production lines.
Innovation Solution
A computer-implemented method that adapts a combination of physical and virtual devices by acquiring a function list and parameter set, forming configurations, simulating runtime, determining downtimes for each device, and adjusting the device composition to minimize total downtime, leveraging virtual failure probabilities and Monte Carlo simulations to optimize device configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the entire automation software is migrated to the Edge Cloud server infrastructure, then maintenance and operation costs are minimized, but implementation effort and installation costs increase significantly due to stringent real-time requirements
Solution Approach 1:
The patent segments the automation software into different components with varying real-time requirements. Critical real-time functions remain on physical devices (PLCs, IPCs) while less time-sensitive functions are migrated to the Edge Cloud server. This segmentation allows maintenance cost reduction through virtualization while avoiding the implementation complexity of fully virtualizing all functions.
Solution Approach 2:
The patent applies local quality by assigning different deployment strategies to different software components based on their specific real-time requirements. Each function is evaluated individually to determine whether it should run locally on physical devices or be migrated to the Edge Cloud, optimizing the balance between maintenance effort and implementation complexity for each component.
2Reliability
If physical devices are used for automation control, then real-time execution requirements are met, but maintenance and operation costs increase
Solution Approach 1:
The patent creates virtual copies of automation functions that run on the Edge Cloud server infrastructure. These virtual device instances replicate the functionality of physical devices for non-critical functions, reducing the need for physical hardware maintenance while preserving real-time execution for essential operations through selective virtualization.
Solution Approach 2:
The Edge Cloud server infrastructure provides universal support for multiple automation functions through virtualization. A single server system can host multiple virtual device instances, replacing multiple physical devices and reducing maintenance costs while maintaining the ability to meet real-time requirements for critical functions.
3Ease of manufacture
If virtual devices are deployed in the Edge Cloud, then maintenance costs are reduced, but downtime increases due to virtual failure probabilities
Solution Approach 1:
The patent implements beforehand cushioning by maintaining physical devices as backup infrastructure alongside the virtualized Edge Cloud system. When virtual devices experience failures or downtime, the system can fallback to physical device execution, cushioning the impact of virtual device unreliability and minimizing overall system downtime.
Solution Approach 2:
The patent creates a composite device architecture that combines virtual devices and physical devices into a hybrid system. This composite approach leverages the maintenance advantages of virtualization while incorporating the reliability of physical devices, achieving a system that benefits from both deployment models without fully committing to either.
Data Source
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AI summary
The invention relates to a computer-implemented method for adapting a device assembly of physical and virtual devices with respect to downtime, wherein the method comprises: forming n configurations of x devices, wherein all x functions of the function list (12) are executable with the devices of each configuration (8); performing a runtime simulation and determining a downtime (2, (4) for each of the physical devices of the parameter set; determining a downtime (3) for each of the virtual devices of the parameter set using the virtual failure probability assigned to the respective virtual device; determining a total downtime (6) for each of the n configurations (8) using the downtimes (2, 3, 4) for each of the physical and virtual devices of the respective configuration (8); and adapting the device assembly according to the configuration (8) with the lowest total downtime (10).