Applications around the Industrial Internet of Things (IIOT) have mushroomed and each one comes with a different set of capabilities and features. So how do you compare different applications or services? And how does the new solution fit into your existing data architecture?
In general, industrial internet of things architectures fall into three categories: (1) on-premise, (2) cloud based or (3) a hybrid of the two. In the on-premises solution, data are never leaving the manufacturing network, whereas in the cloud solution all data are directly send to the cloud. In the hybrid solution, a subset of the data is replicated to the cloud and used for analysis.
Today, many industrial internet of things applications fall into the hybrid category and lead to a scenario where some applications will execute on-premise and others in the cloud. To choose the right blend of on-premise and cloud functionality, let’s consider the following key metrics:
For regulated industries, there is often a requirement that the compressed timeseries is identical between two components.
For a sequential system, the calculation is as follows:
R=R1×R2×R3× ... ×Rn=ΠRj
As an example, if a system has four components with a reliability of 95% each, the overall reliability drops to 81.4%.
Making the same system redundant increases the overall system reliability to:
R=1-(1-R1)×(1-R2)×(1-R3)× ... ×(1-Rn)=1-Π(1-Ri) or 96.6% using Ri=81.4%
Highbyte is providing in flight data contextualization on the edge. This opens the door for very flexible and dynamic solutions.
Most of the protocols are equipment centric, missing relational information (one-to many and many-to-one) and time segmentation. Microsoft’s Digital Twins Definition Language (DTDL) is a relative new approach that has the potential to bridge the gap.
Industrial internet of things apps range from pure on-premises to all cloud-based solutions. On-premises architectures typically provides a higher system reliability and lower latency, while cloud-based solutions offer scalability, flexibility, and wide range of readily accessible data analytics. As a result, manufacturing IT will most likely have a blend of both, where process level analysis will run on premise and enterprise level analytic in the cloud.
Current connectors do not provide a complete manufacturing process model, industrial strength data compression, and redundancy necessary to seamlessly integrate into existing on-premises data architectures. But this is changing quickly and new approaches of in-flight contextualizer are closing the gap quickly. The goal being to better understand and utilize industrial internet of things data.
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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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