Machine Learning (ML) will undoubtedly transform manufacturing and grow from a few selected application such as Predictive Maintenance to a wide range of use cases. The technology already exists today, libraries are widely available under open-source licenses and on-premises IT infrastructure as well as cloud service allow these applications to scale.
So, what is holding it back?
One area that limits the wide adoption of ML models is the underlying data structure. Companies have heavily invested in their data infrastructure and the creation of meta databases (mostly ISA-95 and ISA-88), but the productizing of ML models is still lagging. There are several reasons for this:
Industrial standards ISA-95 and ISA-88 provide a framework to structure the equipment and batch model, but by design do not support ML modeling. For example, one equipment can have several ML uses cases that all require a different structure, e.g. example multivariate batch modeling, predictive maintenance, forecasts for predictive control, …
One approach to structure industrial models is ML Relational Mapping (MLRM). It builds on the already existing object relational mapping (ORM) by linking existing type systems. The concept does not require restructuring existing data models and is therefore fast to implement:
MLRM adds an additional type or class that links for example equipment and batch types as well as provides definitions for the ML model. By separating the functionality, this approach does not clutter the existing type system and provides the flexibility to define different models for one class or multi class models without the need to restructure.
The following shows an OSIsoft AF based UI that implements MLRM:
Machine Learning applications will show grow rapidly in the Manufacturing Environment. The challenge will be to provide the right structure, so that ML models can be built on top of existing type systems. ML Relational Mapping (MLRM) provides a flexible approach by implementing a model specific type system that links to existing data models.
TQS Integration has continued to globally exceed acquisition targets through the development of new customer and partner agreements across many different sectors. As a result of this, they have exceeded their employee growth projections by 20% this year resulting in 75 new employees being hired across all functions of the business.
“We have recruited for these roles all over the World as we continue to support our Global Clients from our Offices in Ireland, Europe, the US and Asia” comments Stephen Quinn, Global Head of HR at TQS.
TQS Integration is happy to announce that part of their business strategy for 2021 will also see a further projected growth of 20% employee attrition globally. “We pride ourselves on being awarded “Waterford’s Best Place to Work” earlier this year and will aspire to grow our employee culture year on year” says Stephen.
About TQS Integration
TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.
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