Edge Gateway Data Modeling for Industrial Device Discovery
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Solution Overview
Problem
The process of extracting operational and diagnostic data from industrial devices for use in visualization and analytic systems is complicated, requiring cooperation between IT and OT experts, and much of the data is uncontextualized, necessitating external applications to define its meaning.
Innovation Solution
A cloud-based Edge as a Service (EaaS) system that uses device profiles to automatically discover and retrieve relevant data items from industrial devices, contextualize them using information models, and egress the data to external applications, allowing centralized configuration and management of edge devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data extraction is performed using traditional methods requiring IT and OT expert cooperation, then data can be correctly interpreted and understood, but the process becomes complicated and time-consuming
Solution Approach 1:
The system enables self-service data extraction and contextualization by automatically discovering devices on the network, selecting relevant data items based on device profiles, and applying information models without requiring manual configuration by IT or OT experts. The automated pipeline performs data extraction, contextualization, and enrichment autonomously.
Solution Approach 2:
An automated data integration pipeline acts as an intermediary between industrial devices and external applications. This pipeline includes components for data extraction, contextualization using information models, and enrichment, thereby simplifying the complex interaction between IT and OT domains while maintaining data interpretation accuracy.
2Ease of manufacture
If uncontextualized, unstructured data is collected from industrial devices, then data collection is simplified, but the data lacks meaning and requires external applications to define its context
Solution Approach 1:
The system performs preliminary contextualization by automatically selecting relevant data items based on device profiles and applying information models during the data extraction phase. This preliminary action embeds contextual meaning into the data before it reaches external applications, eliminating the need for them to define data context from scratch.
Solution Approach 2:
The automated data integration pipeline serves as an intermediary that enriches unstructured data with contextual information using information models and device profiles. This intermediary component adds meaning to the data during transmission, preventing information loss while maintaining collection simplicity.
3Loss of information
If manual configuration and setup is performed for data extraction systems, then data can be properly contextualized, but the setup time and effort increase significantly
Solution Approach 1:
The system automatically performs configuration tasks by discovering devices on the network, selecting appropriate device profiles, identifying relevant data items, and applying information models without requiring manual setup. This self-service approach eliminates time-consuming manual configuration while maintaining proper data contextualization.
Solution Approach 2:
The system performs preliminary configuration by pre-defining device profiles and information models that can be automatically applied during data extraction. This preliminary preparation enables the system to contextualize data automatically without requiring manual setup time during deployment.
4Adaptability or versatility
If external applications are required to define data meaning, then data can be customized for specific applications, but the overall system complexity increases
Solution Approach 1:
The system uses universal information models and device profiles that can be applied across multiple applications and device types. These standardized models provide a common framework for data contextualization that maintains adaptability to different applications while reducing integration complexity through reuse.
Solution Approach 2:
The automated data integration pipeline acts as a universal intermediary that applies standardized information models to contextualize data for different applications. This intermediary layer maintains data customization flexibility through configurable profiles while reducing overall system complexity by providing a unified approach to data preparation.
Data Source
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AI summary
A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.