AI-Based Field Device Configuration for Better Measurement Accuracy
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Solution Overview
Problem
Field devices in automation systems are complex and difficult to configure, requiring specialized knowledge and on-site service technicians to optimize measurement performance amidst various interference variables and application-specific parameters.
Innovation Solution
A procedure using machine learning and a central data storage system to generate configuration data tailored to specific applications and environmental conditions, allowing for automated or assisted configuration of field devices, leveraging AI and cloud-based databases for data collection and access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If field devices are configured manually by service technicians with specialized knowledge, then measurement performance can be optimized for specific applications, but the configuration process becomes complex and time-consuming
Solution Approach 1:
The field device automatically configures itself by receiving environmental information from sensors and using the adaptive learning program to determine optimal configuration parameters without requiring manual intervention by service technicians. This self-service approach resolves the contradiction by eliminating configuration complexity while maintaining measurement precision through AI-driven automatic optimization.
Solution Approach 2:
The system dynamically adjusts configuration parameters of the field device based on environmental conditions detected by sensors and processed by the adaptive learning program. This allows measurement performance to be optimized for specific applications through automatic parameter adaptation, resolving the contradiction between achieving precise measurement and avoiding complex manual configuration.
2Adaptability or versatility
If service technicians perform on-site configuration and troubleshooting, then field devices can be properly configured for specific applications, but specialized knowledge and expertise are required
Solution Approach 1:
The field device automatically adapts to specific applications by receiving environmental information and using the adaptive learning program to determine optimal configuration parameters. This eliminates the need for service technicians with specialized knowledge while maintaining application-specific adaptability, thereby resolving the contradiction between versatility and ease of operation.
Solution Approach 2:
The adaptive learning program acts as an intermediary between environmental sensors and field device configuration, automatically translating environmental conditions into optimal configuration parameters. This intermediary system enables application-specific adaptation without requiring human expertise, resolving the contradiction between adaptability and operational ease.
3Productivity
If traditional configuration methods are used without AI assistance, then service technicians can control the configuration process, but time and manual intervention are required
Solution Approach 1:
The field device automatically configures itself using environmental information and the adaptive learning program, eliminating the need for manual intervention by service technicians. This self-service automation dramatically increases configuration productivity while achieving full automatic configuration, thereby resolving the contradiction between productivity and extent of automation.
Solution Approach 2:
The system replaces the mechanical process of manual configuration by service technicians with an automated information processing system using sensors, communication interfaces, and machine learning algorithms. This substitution enables automatic configuration while improving productivity, resolving the contradiction between extent of automation and configuration speed.
4Measurement precision
If field devices are exposed to various environmental disturbances, then real-world measurement accuracy is challenged, but configuration must account for these conditions
Solution Approach 1:
The system performs preliminary anti-action by detecting environmental disturbances through sensors before they affect measurement accuracy, and the adaptive learning program pre-adjusts configuration parameters to compensate for these harmful factors. This proactive approach maintains measurement precision despite environmental disturbances, resolving the contradiction between measurement accuracy and environmental challenges.
Solution Approach 2:
The system continuously monitors environmental conditions through sensors and uses feedback from the adaptive learning program to dynamically adjust field device configuration parameters. This closed-loop feedback mechanism maintains measurement accuracy by compensating for environmental disturbances in real-time, resolving the contradiction between measurement precision and harmful environmental factors.
Data Source
Figure 1~4
AI summary
The invention relates to a method for improving the measuring performance of a field device (1) which is to be configured, is installed in a defined application and determines or monitors at least one defined physical or chemical process variable in an automation installation, having the following method steps: a multiplicity of field devices (1) which determine or monitor different physical or chemical process variables in different applications (A) and under different environmental conditions (UB) prevailing at the respective measuring positions of the field devices (1) are configured or parameterized by means of a configurations tool; the configuration data, the information relating to the respective applications (A) and the information relating to the environmental conditions (UB) of the individual field devices (1) prevailing at the respective measuring positions are stored in a central data memory (4) as training data, the training data are made available to an adaptive computing program (5) which uses at least one artificial intelligence method; for the purpose of configuring the field device to be configured, current information relating to the particular application (A) and the environmental conditions (UB) prevailing at the measuring position of the field device to be configured are made available to the adaptive computing program (5); on the basis of the current information, the adaptive computing program (5) provides the field device (1) to be configured with configuration data (3) on the basis of the multiplicity of training data, which configuration data are matched to the particular application (a) taking into account the environmental conditions (UB) prevailing at the measuring position of the field device (1) to be configured.