Industrial Internet of Things, IIoT usecases, automation stack or 5 layer model, predictive maintenance
INDUSTRIAL INTERNET OF THINGS (IIoT)
The idea of covering the industrial internet as a broader use case of the internet of things is that it turns out to be the most enterprise-friendly or commercially most viable application of the internet of things for enterprises. When we talk about the industrial internet of things, it is often confused with the usage of the internet of things only on the manufacturing shop floor. Whereas the idea behind having a structured approach to enterprise internet of things is that if enterprises can make use of internet of things technologies for various purposes involving the end-to-end business the internet of things. When we look at the enterprise nature of the application of industrial internet in that sense the set of verticals where such a concept can be applied ranges from manufacturing shop floor to transportation. Where we can put all kinds of sensors and containers, etc. to retail to high-tech manufacturing to ports, etc.
Hence, the definition of the industrial internet is broad to cover a broad range of
verticals. So if you see from a perspective of where this whole concept of the industrial
internet has appeared there have been two broad origins. The terminology for an
alternative approach to the similar thing was industry 4.0. Industry 4.0 was an
approach started and pioneered by German industry bodies to be prepared for the
next wave of automation in the industry. Where they talked about the fourth wave
of automation. Starting from the first wave is power, electricity generation. Then
the second wave as machines, industrial evolution machines driven industrial
evolution. Third-wave as information technology revolution-based disruption. Now
the fourth industrial revolution they term as when computing and communication
can be embedded into everything around us. Thereby opportunities for innovation
and disrupting the industry are multi-fold. This terminology is termed industry
4.0 which is making the whole industry amenable to extreme automation and visibility.
Also,
the industrial internet of things appeared more from a generic approach to
applying the concepts of the internet of things in the industry whether
it is manufacturing transportation, etc.
While
industry 4.0 was focused on manufacturing as a broad vertical where this effect
of industrial automation using the internet of things will be experienced. But IIoT or
the industrial internet of things takes into account many dimensions of many
industries and does not restrict itself to pure manufacturing. Therefore, if you look
at the idea of understanding the application of IoT in different verticals. It
is ranging from how IoT can help in business processes across industries. The
industries could be like the German approach to industry 4.0 could be covering
manufacturing end-to-end including shop floor. It could include transportation,
electronics, hardware, and other industries including the food industry. Where
there's a lot of elements of the internet of things being used for automation, sensing,
and good quality, etc.
IIoT Use Cases
The
broad use cases of the internet of things have emerged as strong contenders for
technology usage in the industry. These can be categorized into things
like machinery overall life cycle, environment life cycle, air, water quality life-cycle,
asset management, supply chain, automotive, and intelligent buildings. If you
look at from a machinery and factory shop floor etc. There are several variations
of the applications. Primary among them could be a diagnosis of machinery in
terms of understanding if there is a fault or a crack or malfunction happened.
Predictive Maintenance
Predictive maintenance is about being able to predict if some crack is going to appear or if there is going to be a disruption. Because of the wear and tear of a conveyor belt. Preventive maintenance is an extension of predictive maintenance with a focus on making sure that we take remedial action.
Asset Perspective
From an asset perspective, we look at different asset lifecycle management scenarios where IIoT has been deployed and will be used going forward in a very wide and large-scale manner. Asset monitoring is a key important requirement for making sure that we keep track of assets prevents them from getting lost or stolen. Attaching tags or sensors to assets is a brilliant idea in that case. In the same way, if you want to track locations of assets where they are to like inventory. Beyond the factory shop floor and asset management.
Environment as a key use case for IoT
A smart environment built with embedding IoT in all aspects of air-conditioning, coolers, storage, energy-guzzling lamps, and fans, etc. These are all the approach is to make the environment smart, consumes less energy have visibility into energy monitoring. And also actionable intelligence out of usage-based on which we can turn on or off intelligently the power sources. Like a room unless somebody enters or lights may be switched off. In the same way, we can look at monitoring HVAC and air conditioners and switch them on only when there are some people around. This is only possible if you have a smart environment and we have sensors monitoring different aspects of the environment.
Pollution Control
As a continuation of the environment monitoring is the notion of pollution control, pollution visibility. So across cities, we are nowadays looking at several deployments of IoT-enabled sensors and dashboards which are giving out real-time values of pm 2.5 or pm 10, etc. of different toxic and harmful pollutants like CO2, NO2, etc. These are the ideas of giving visibility into the pollution levels at different locations in a city depending upon the kind of metric. Sometimes these may be looking at values of temperature or humidity. Whereas in some other cases we may be concerned with the values of particulate matter which is what pm 2.5 or pm 10 indicates.
The
combination of sensors, data analytics, machine learning, and the internet of things
is completely transforming the way that we live and work. Technologies we may
never have dreamed possible. Except in science fiction have become a reality
and an integral part of our daily lives. Advances in technology make it
essential for engineer’s technical professionals and business leaders to
understand concepts such as artificial intelligence, machine learning, blockchain,
sensors, and beyond.
Automation Stack/Automation Pyramid/5 Layer Model
Let's
go one step back from industry 3.0 into the third revolution. In third
revolutions, we are becoming digital and automation. In the automation stack, it
is also called the automation pyramid or the five-layer model.
And
what this model does? If you want to produce a product in an industrial
environment in the digital age. You are there on the enterprise resource planning
system and planning what you want to produce. You cut all your POS with your
customers you have all your supplier relations or employee-based files all that
is in the ERP file system. Then one step down to your plant level to the
manufacturing execution system. Again one step down to the shop floor level and
the supervisory control and data acquisition system SCADA. Finally, plant level
you're getting into the PLCs the programmable logic controllers. Then the field
level the input and output signals and also the human-machine interfaces.
And
finally, your production producing end products. Now once you produce something
you are collecting data. The data is collected through your input and output brought
into the PLC given to the SCADA system from the state it goes up to MES and ERP.
So planning is going from the bottom to downwards and your data feed is from the bottom
up. So what you need is interfaces between layers. There are two major
difficulties with that model.
We
have many PLCs and machines producing end products. All those machines and PLCs
are produced by some OEM and have their proprietary interface. So if we want to
collect with our SCADA system all the data from PLC's we have to implement thousands
of various proprietary standards. So this is the point where we have to get more
to open data models, data transparency, information models, etc. Now the SCADA system
is done by an integrator or by the plant themselves. In the SCADA system, a lot
of monitoring of what happens on our shop floor monitor, trends, and feedback for
it.
But the valid point is to feed this information back into the MES system. MES is also done by some integrated. But it's a different integrator and between the MES and the SCADA. There is no digital connection this is where people are still using routing sheets you have some you plan your production in your MES you print it you put it on the shop floor and then it goes from with the component from a machine. So that's on the way downstream data is collected in your SCADA systems going upstream. But you want to have your real-time information out of your production combine it with all the financial data you have in the ERP to learn something about how to improve your production. So these are the two points where all the ideas of the automation stack which is coming from industry 3.0 where this one has its issues.
So now coming from the age of digitization and a digital transformation is the industrial internet of things. So what we do to the IIoT is we get rid of all those connections, and we replace it with the IoT by one place where we have all the data connections going in and out of this one industrial internet of things we are using every day. Now we need data transparency and semantic interoperability. So that we can help industrial production, industrial maintenance, industrial design to make better products to make better designs longer lifetime and to help everybody also with sustainability.
COMMENTS