IEC 61499 Asset Modeling for Distributed Control Auto-Configuration
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Solution Overview
Problem
Current industrial process automation systems face challenges in efficiently designing, commissioning, and maintaining distributed control systems, particularly in terms of zero engineering efforts and seamless integration of distributed intelligence across various devices.
Innovation Solution
The implementation of an IEC 61499 standard-based system that allows for the modeling and auto-creation of asset-based control applications using a processor and storage memory, enabling the configuration of distributed control systems through an asset model and library of distributed control assets with predefined facets, and utilizing machine learning or an asset configurator tool for auto-creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional industrial process automation systems are used for designing and commissioning distributed control systems, then system functionality is achieved, but engineering effort and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining standardized asset models with built-in facets and control logic before actual system commissioning. These pre-configured models can be directly instantiated and mapped to physical devices, eliminating the need for time-consuming on-site programming and configuration work during the commissioning phase.
Solution Approach 2:
The patent uses copying by creating reusable asset models that can be instantiated multiple times across different devices and systems. These standardized models serve as templates that can be copied and adapted, significantly reducing the engineering effort required for each new deployment compared to creating custom solutions from scratch.
2Adaptability or versatility
If distributed control systems are designed with high flexibility and adaptability, then system versatility improves, but system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the complex distributed control system into standardized, modular asset models with defined facets. Each asset model represents a discrete functional unit that can be independently configured, mapped to specific devices, and managed separately, thereby reducing overall system complexity while maintaining flexibility.
Solution Approach 2:
The patent implements universality by designing asset models with built-in facets that can serve multiple functions and be applied across different device types and control scenarios. These universal models can be instantiated and adapted to various physical devices, providing system versatility without requiring separate custom designs for each application.
3Manufacturing precision
If manual configuration and programming of distributed control systems is performed, then precise control is achieved, but engineering effort and cost increase
Solution Approach 1:
The patent applies self-service by enabling automated mapping between asset models and physical devices, where the system automatically generates control logic and configurations based on pre-defined asset models. This automation maintains control precision while eliminating manual programming efforts, allowing the system to configure itself with minimal human intervention.
Solution Approach 2:
The patent uses preliminary action by pre-configuring asset models with control logic and parameters before deployment. This advance preparation ensures control precision is built-in from the start, while the actual system configuration during commissioning requires minimal manual intervention, as the pre-configured models can be directly instantiated and mapped to devices.
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AI summary
Configuring distributed control in an industrial system comprises building an asset model representative of a process control installation of the industrial system and creating an asset library of distributed control assets according to a distributed control programming standard. The asset model includes modeled assets defined according to levels of a physical model standard and representing physical devices of the industrial system. The distributed control assets each have one or more predefined, built-in facets. One of the distributed control assets in the asset library is mapped to each of the modeled assets to configure the process control installation of the industrial system and generate an asset-based control application for providing distributed control of the industrial system. Additional aspects relate to auto-creation of control applications based on an information model, either through the use of machine learning or an asset configurator tool.