OPC UA Diagnostic Data Collection for Automated Plant Setup
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Solution Overview
Problem
Setting up a collecting of diagnostic and monitoring data in an industrial plant, particularly for distributed control systems, requires substantial manual configuration efforts and is prone to errors, making it costly and inefficient.
Innovation Solution
A computer-implemented method using an OPC UA server to automate the discovery, configuration, and connection of diagnostic data to a time-series database, involving the provision of diagnostic metadata, creation of data collection units, and generation of configuration files to facilitate automated data transfer and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual configuration is used to set up diagnostic data collection in a distributed control system, then the system can be configured with existing tools, but substantial manual effort and time are required and errors are prone
Solution Approach 1:
The system performs self-configuration by automatically discovering OPC UA servers in the network, retrieving their diagnostic metadata, and generating appropriate data collection units and database configurations without requiring manual intervention. The diagnostic unit autonomously browses the OPC UA address space, extracts node information, and configures the time-series database schema based on the discovered diagnostic data structures.
Solution Approach 2:
The system performs preliminary actions by pre-defining the configuration templates and data collection unit structures before actual deployment. The metadata from OPC UA servers is retrieved and processed in advance to generate configuration files and database schemas, so that when components are added to the plant, the configuration is already prepared and can be automatically applied.
2Productivity
If automated discovery and configuration is implemented, then setup time and manual effort are reduced, but system complexity increases due to additional automated components
Solution Approach 1:
The diagnostic unit serves multiple functions: it discovers OPC UA servers in the network, retrieves and parses their diagnostic metadata, generates data collection unit configurations, creates time-series database schemas, and configures the entire data collection pipeline. This multi-functional approach consolidates what would otherwise require multiple separate tools and manual steps into a single automated system.
Solution Approach 2:
The system introduces a diagnostic unit as an intermediary component that mediates between the OPC UA servers and the time-series database. This intermediary automatically translates OPC UA diagnostic data structures into database-compatible formats, handling the complexity of protocol translation and configuration generation while presenting a simplified interface to both sources and destinations.
3Ease of manufacture
If manual configuration is used, then system architecture remains simpler, but configuration errors increase and cost increases
Solution Approach 1:
The system implements feedback mechanisms where the diagnostic unit continuously monitors the configuration status, verifies that discovered OPC UA servers are properly connected to the data collection units, and validates that the time-series database schema correctly represents the diagnostic data structures. This automated verification reduces configuration errors by detecting and reporting issues before they affect system operation.
Solution Approach 2:
The system creates accurate copies of the OPC UA diagnostic data structures in the time-series database schema. By automatically browsing the OPC UA address space and replicating the node hierarchy, data types, and relationships in the database schema, the system ensures configuration accuracy without manual intervention, reducing both errors and implementation costs.
Data Source
AI summary
A method for providing diagnostic data of a component comprising an OPC UA server includes providing metadata that describes the diagnostic data; discovering the OPC UA server; creating a corresponding data collection unit comprising an OPC UA client; generating a configuration file using the metadata; transferring the configuration file to the data collection unit; modifying a configuration file of the time-series database by adding a URI of the data collection unit; connecting to the time-series database; delivering diagnostic data of the component via the data collection unit to the time-series database; and storing the diagnostic data of the component to a diagnostic data repository of the time-series database.