What is IIOT? IIoT is all about data generated by industrial connected devices and integrated with IT to help operators achieve further efficiency.
Data generated by IIoT devices can help operators meet various objectives, for example predictive maintenance and downtime minimization. Yet securing this data has also become critical amid rampant cyberattacks. Meanwhile, the data generated by the operational side of a production facility is more and more integrated with IT to help operators achieve further efficiency.
IIoT is all about data generated by industrial connected devices. This will entail subsequent mining and analysis of data by professionals. However, manufacturers facing budget constraints are not necessarily in a good position to hire these professionals. This is where artificial intelligence and machine learning come in.
“There is a shortage of qualified Big Data analysts, engineers and scientists in the labor market. Industrial plants do not have the flexibility to create robust centers of excellence, and plant-level reliability and maintenance technicians cannot be expected to acquire machine learning expertise. The result is that IIoT vendors will be forced to provide solutions that do not require significant input from plant employees,” said Eitan Vesely, CEO of Presenso
. “In our case - IIoT predictive asset maintenance - we use automated machine learning. This means that our system performs automatic feature extraction, model calibration (also known as meta-parameter search or hyper-parameter optimization) and model selection, to select the most appropriate anomaly detection model for each sensor to be analyzed. In other words, our model does the work of a data scientist without the need to hire one.”
With an overwhelming amount of data generated, making sure this data is securely transmitted and stored becomes key. As a result, cybersecurity in IIoT must be properly addressed.
“The benefits of using big data analytics to optimize a manufacturing process can be completely wiped out in an instance if the same connectivity from the sensors to the cloud can be used to hijack manufacturing processes and holding companies for ransom to release control by digital extortionists,” said Howard Wang, Director of Sales for APAC/ROW at Real-Time Innovations
. “As systems not previously accessible via the internet now have a global network presence, the digital security of IIoT systems will play a large part in whether or not IIoT systems succeed or fail to deliver on the promises of IIoT. Safety and security go hand-in-hand.”
For a long time, the operational technology (OT) and information technology (IT) at a manufacturing facility have been working independently. But with IIoT, a convergence between OT and IT has become possible, bringing various benefits to users. “Operation's focus is on ensuring uptime, and IT is focused on standards, ensuring the integrity and confidentially of data. Now both are being asked to help the business become more agile, efficient and derive more value out of their investments,” said Eric Ehlers, Vertical Marketing Manager at Cisco
“IT and OT have been operating in silos in different ecosystems, with divergent goals. The gap between these two facets is impeding the manufacturer’s path towards achieving operational efficiency. But as IIoT, big data and smart machines gain traction in the industrial landscape, manufacturers are realizing the benefits of bringing IT and OT together,” said Keshab Panda, CEO and Managing Director of L&T Technology Services
. “Data generated from OT can be correlated by IT systems, paving the way for optimizing business processes, reducing operational costs, acquiring information for better decision making, better production planning and logistics.”
A main difficulty in this OT-IT convergence has been interoperability between different data formats. More and more this has been resolved. “Using our solution as an example, when we built our predictive maintenance solution, interoperability was a key consideration. We did not limit ourselves to machine type or data format because we recognize the lag between the adoption of OT and IT in most industrial plants,” Vesely said.
“What is needed is a powerful, standards-based connectivity framework that can be used as a normalized databus in which the myriad of peripheral protocols (often proprietary in nature) can be translated via gateways and enable systems that were not designed to connect to each other to be quickly integrated and achieve the great benefits foreseen in IIoT. Examples of such connectivity frameworks are OMG’s DDS (Data Distribution Service) and OPC Foundation’s OPC UA,” Wang said.