IO-Link Parameterization Using an Expert Configuration Assistant
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The parameterization of IO-Link devices, especially complex condition monitoring sensors, requires significant technical expertise and effort due to numerous parameters and complex dependencies, making it difficult to adapt these devices for new or modified applications effectively.
Innovation Solution
An automated method using an expert system and configuration assistant that converts user-provided application knowledge into a suitable parameter set, either through machine learning or rule-based approaches, allowing for simplified and automated parameterization of IO-Link devices, including consideration of parameter dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameterisation via IODD or controller is used, then device functionality is achieved, but technical expertise and configuration time are significantly required
Solution Approach 1:
An automated parameterisation device acts as an intermediary between the user and the complex IO-Link device parameters. This intermediary automatically translates application requirements into appropriate parameter settings, eliminating the need for users to manually navigate complex parameter lists and understand technical dependencies.
Solution Approach 2:
The system enables self-service parameterisation where the IO-Link device automatically receives and applies optimized parameter settings based on its application. The device self-configures by receiving application-specific parameters through the automated system, eliminating manual intervention and reducing configuration complexity.
2Adaptability or versatility
If comprehensive parameter lists are provided for all IO-Link parameters, then complete device configuration is enabled, but user comprehension and configuration time increase significantly
Solution Approach 1:
The system extracts only the essential application-specific parameters needed for proper device configuration, rather than presenting the complete list of all possible IO-Link parameters. This extraction approach maintains adaptability while dramatically reducing configuration time by focusing only on relevant parameters.
Solution Approach 2:
The system performs preliminary analysis of application requirements and pre-determines the appropriate parameter settings before the user begins configuration. This preliminary action enables the system to present only necessary parameters with pre-calculated optimal values, reducing configuration time while maintaining adaptability.
3Reliability
If detailed documentation and parameter explanations are provided, then user understanding is improved, but configuration complexity and time increase
Solution Approach 1:
The system replaces manual documentation review and parameter analysis with automated electronic determination of parameter settings. Instead of users reading and interpreting detailed documentation, the automated system electronically analyzes application requirements and calculates optimal parameters, improving accuracy while reducing perceived complexity.
Data Source
AI summary
In the case of the method and the device described here for automated parameterisation of at least one IO-Link device (300-310) connected, via an IO-Link connection (315-325) using communication technology, to a device (330) having IO-Link Master functionalities for a predetermined intended use (410) of at least one IO-Link device (300-310) by means of a configuration assistant (400), it is in particular provided that on the basis of the intended use (410) by means of an expert system (430) which is based on an artificial neural network and/or is rule-based, by means of which information (435) relevant for the parameterisation is automatically determined by means of user (445) inputs (440) guided by a configuration assistant (400) serving as a front end for the expert system (430), and a parameter set (455) suitable for the preferably application-related intended use (410) is automatically created for the parameterisation from the determined information (435) relevant for the parameterisation.