TQS Integration, an international software consultancy company specializing in the provision of system architecture and application design, engineering, and system integration, today announced that they have been certified as a Google Partner with Google Cloud Partner Advantage.

Achieving Partner status means that TQS and Google can now share their combined knowledge and advanced technologies to provide the perfect end to end experience for their customers, from edge computing to automation, data historian to advanced data analytics and more.

“Obtaining Partner Status is a huge step into the future. It means recognition of our unique advanced technical expertise, and the ability to mutually share our learnings across a larger customer base with the Google team” says Rory Sheehan, Global Strategic Account Manager.

As a start, TQS and Google will focus their partnership on the Pharma / Lifesciences Industry where they have many mutual customers. Combined, their expertise and systems will allow customers to access manufacturing data, R&D data, operational data, and predictive data to streamline operations and quality.

“Our services will allow data sharing in a much wider capacity, both within the business and externally where required. With this partnership program and all our ongoing TQS developments, we are bringing customers, industries and partners into the future of manufacturing and that future is fuelled by data.” says Rory.

TQS has always been recognised for providing customers with an unrivalled service and advanced engineering, and this new Google partnership will continue to advance them into the future of Industry 4.0, IIoT and Edge Computing.

About TQS Integration

TQS Integration is a global data solution company specializing in the provision of 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 extensive client base in the 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.

For information, please contact us.

Most biotech and pharmaceutical companies are adopting Process Analytical Technology (PAT) in manufacturing to provide real time operational insights that allows better control and leads to higher yields, purity, and\or shorter cycle times.

However, to integrate PAT data into an existing data infrastructure has been challenging. Because each PAT – also a single measurement – is a spectrum consisting of a list or array of values (e.g. time, channel, value) that cannot be stored in a classical data historian.

This graph shows you several spectra forming a multi-dimensional time series:

integrate PAT data

Spectra are often stored in SQL type databases as plain tables, separate from the other manufacturing data stored in the historian. The main problem with this approach is the loss of equipment and batch context. This is problematic to any subsequent analysis.

So why are spectra stored separately? Because most industrial data historian store values as simple time series in different data types, typically bool, int, float and string. Each time series point is a tuple of a timestamp and a single value (scalar). For PAT and other use cases, it would be required to extend the existing data shapes to accommodate vectors, matrices, and tensors:

There are many use cases for these data structures:

In the OSIsoft Asset Framework, extending the Historian is accomplished by deploying a new source and linking it to a time series database that supports time-based vectors, matrices, and tensors:

The RAMAN spectra are attributes on the unit\vessel or located on the RAMAN equipment. Therefore, extending the existing OSIsoft AF data model allows the measurements to be analyzed in the present batch or event frame context:

Conclusion

Classical historians have been developed for scalar time series information. This has worked for most sensor data type, but they cannot accommodate higher dimensional time series information. The solution is to extend the existing historian with databases that allow a more flexible schema. This results in better utilization of existing equipment and batch context that enables context specific analysis.

Please contact us for more information.

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