5.3 Retrofit OPC UA to existing machines
Brown field manufacturing systems may have embedded data models that aren’t articulated in a discoverable fashion but are likely derived from patterns or practices that have broad internal, or vertical industry acceptance – though without detailed implementation design. This “tribal knowledge” of how a piece of equipment or process works can be easily misinterpreted by different personas and runs the risk of getting lost entirely as a workforce shifts or turns over. Capturing these NameSpaces in a semantically reliable, structurally useful fashion, and publishing them for broad consumption, can allow manufacturers to map their systems to normative approaches found in a common library. Not only does this mapping accelerate future information projects on existing machines by providing a common interface and dictionary of terms, but it facilitates interoperability between systems, workforce development, and a reference implementation of industry practices that can be used to derive or develop new standards. A published library of NameSpaces that constituents can draw from enables implementers to begin to speak a “common language” that uses OPC UA as its lexicon.
Specific examples for this use case are:
As a Process Engineer designing a new line, I want to search for NameSpaces for Packaging Machines. I am familiar with PackML, so might search for that by name, or use terms from that standard.
As a supply chain participant for a consumer goods company, I’m required to implement an AddressSpace for my inventory management system that they have published to the Cloud Lib. I’d like to search for it by endorser and application (e.g.: warehouse, or logistics)
As a quality assurance engineer, I want to understand which Extruder OEMs have published NameSpaces about their inspection capabilities. I might search by vendor name, machine type, or quality terms I’m familiar with.
As a Production Manager, I’d like to find an efficiency NameSpaces I can retrofit into my paper converting operation.
As a Process Control Engineer, I would like to build my PLC program so that it fulfills a published NameSpace for a heat treatment process. I might search for “heat treatment” but I’ll also be interested to know if any existing Models have been built by or for my PLC hardware vendor of choice.
As a Machine Builder, I would like to understand what NameSpaces my customers might have found in the Cloud Lib to determine if I should implement my machine to support those models. I might search by industry, problem space, or even customer name.
As a System Integrator, I would like to retrofit the control program for an Automotive paint line, so that it meets the requirements of a newly published NameSpaces standard suggested by the Auto Industry.
As an AI Researcher trying to build a predictive energy model for smelting operations, I would like to search the CloudLib to see what NameSpaces the industry is using, so I can determine what data might be available for training my model.
Requirements for this use case include:
Comprehensive set of existing (standardized) OPC UA info models pre-set in UA Cloud Lib, logically grouped by machine type (can be a query API feature)
“Mapping Guide” for doing the actual mapping (can be supplied separately)