What is the Industrial Internet of Things (IIoT)?
Automation and Internet Cloud Computing are evolving and merging into a space allowing for Artificial Intelligence (AI) programming to facilitate improvements in productivity and efficiency with many economic benefits.
Along with Internet Cloud computing and other advancements with the Internet, we are now in the middle of the next industrial revolution mining and turning data into information.
This period of evolution is called Industry 4.0 or the Fourth Industrial Revolution which includes Industrial Internet of Things (IIoT) and Digital Transformation mechanisms that facilitate this evolved 4th generation period of Automation.
In this lesson, we will learn about, What is the Industrial Internet of Things (IIoT)?
IIoT Definition vs. IoT
While the Industrial Internet of Things does fall under the Internet of Things (IoT) area of influence, the key difference is IIoT concentrates on the connection of machines and devices in industries like manufacturing, healthcare, and logistics.
IoT is commonly used to define consumer-based devices like Fitbits, and a wide range of smart home devices for example refrigerators, ring video doorbells, lighting, thermostats, and alarm systems.
IIoT is about getting information that every single consumer need in their hands when they want it. Digital transformation is the digitization of a business in how we must move to a unified organize data space using the International Society of Automation standard for developing an automated interface between enterprise and control systems or ISA95.
ISA95 hierarchical model
ISA95 essentially provides a hierarchical model for the Enterprise, Site, Area, Line, and Cell.
Each level of this architecture uses a specific software system to gather data for that part of the organization needs, in which creates a problem for communication between these disparate applications.
IIoT real-time data
With most organizations, the data is not real-time and lacks efficiency inhibiting key stakeholders to make informed decisions. IIoT is about making decisions from information received real-time and not from a report created from yesterday’s data.
The Industrial Internet of Things is all about interconnected instruments, sensors, and many other devices. They are networked together and communicating with computer-driven industrial applications for many manufacturing and energy management areas of business.
IIoT is an evolution of a control system that allows for a significant improvement in automation by using cloud computing to enhance and optimize process controls.
IIoT key technologies
Industrial IoT depends on many technologies but the key technologies primarily consist of Artificial intelligence, Cyber security, Cloud computing, Edge computing, and Data mining.
1) Artificial intelligence (AI) & machine learning (ML)
Artificial intelligence (AI) and machine learning (ML) are fields that are part of computer science.
AI is where intelligent machines are developed and respond like humans. ML is where machine learning is a part of AI predicting and a more accurate outcome delivered without programming.
2) Cyber security technology
Cyber security technology becomes an important basic platform for IoT and IIoT enabling disconnected machines to physically connect and communicate in a secure method.
3) Cloud computing
Cloud computing is basically using IT services and the files to be uploaded and downloaded from Internet-based servers opposed to using local extranet-connected servers.
4) Edge computing
Edge computing is a distributed computing model bringing data storage closer to the location where it is needed and optimizes sensors, industrial computers, and devices that are part of the IIoT system for publishing and consuming data for faster processing.
5) Data mining
Data mining and analytics are about collating and examining large amounts of data stored from various parts of the enterprise.
OK, so why should we go through this somewhat major transformation, and what are the benefits?
Companies want to be more competitive, increase efficiency with just-in-time manufacturing in meeting higher customer demand with better inventory control. We can do all of this with the creation of the organizations’ overall automation a digital twin.
A digital twin is a virtual representation referring to a digital replication of the actual companies’ physical assets, processes in place, automation systems, and devices. The twin uses real-time data to enable learning and reasoning for improved decision making.
The digital twin allows experimentation with new information that is generated by cloud-based AI functions without having to shut-down production or be concerned with personal safety because testing is performed in a virtual space.
Also, the digital twin could be used as a training ground for new employees without impacting the live system. And when comparing IIoT and IoT failures, IIoT create much higher risks than IoT. Life-threatening situations or major financial losses could happen from an actual system failure or downtime for example.
IIoT concerns and risks
While we may benefit from a long list of advantages through the IIoT transformation, there are some concerns and risks we should be aware of.
Some of the potential risks of adopting IIoT are the expense of cost of data integration, lack of experience, and difficulty of implementation, and the devastating cyber threats.
Data Integration is one of the biggest obstacles to IIoT implementation. You could be looking at building a system with thousands of existing connected sensors and devices, adding new equipment and software systems, and interfacing legacy equipment to communicate using standard IIoT protocols.
There is a high cost of integration preparing for IIoT, requiring new software, hardware, and equipment. You will need to consider the costs associated with training your existing workforce and hiring new staff and the time needed to get workers productive.
And the lack of expertise with integrating IIoT requiring automation companies to be fully experienced with an organization’s layers of systems starting with the plant floor transmitters, process PLCs, operation HMI, and SCADA, reporting based on Database Administration, manufacturing execution systems (MES) for warehouse control and Enterprise Resource Planning (ERP) for accounting systems.
Now integrators are required to have expertise in machine learning, data science, and real-time analytics.
In the long term, IIoT could more than pay for itself, but many organizations are still justifiably concerned about investing so much in IIoT.
On the bright side, the benefits of IIoT in manufacturing, pharmaceuticals, agriculture, mining, Oil and Gas, and retail are gaining ground and many companies are embracing this new paradigm for solutions in optimizing operations and personnel productivity.
Many manufacturers have already achieved process performance and have reductions in downtime. And while manufacturing is certainly leading the way with IIoT adoption, other industries are becoming open to the idea of embracing IIoT for example.
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The RealPars Team