What is the new era of the Industrial Internet of Things?

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What is the new era of the IIoT?
Written By Calio
What is the new era of the Industrial Internet of Things?
Since 2016, the IT industry has set off a wave of artificial intelligence (AI). The integration of AI
and the Internet of Things will become the mainstream system in various vertical fields in the
future. In manufacturing, AI will also become one of the core computing architectures of the
Industrial Internet of Things.
Intelligence is the most important trend in manufacturing in recent years. After several years of
market education, market inquiry has started to increase in the past two years. From 2016, the IT
industry has set off a wave of artificial intelligence (AI). AI and the Internet of Things Integration
will become the mainstream system in various vertical fields in the future. In manufacturing, AI
will also become one of the core computing architectures of the Industrial Internet of Things.
Since Germany took the lead in calling out Industry 4.0, related technologies have also made
rapid progress, including the development of industrial Internet of Things, big data analysis,
robotics, and other technologies. So far, new types of smart factories and new industrial
standards have gradually been created.
Especially in recent years, the surge of artificial intelligence (AI) has given Industry 4.0 a
brand-new development direction. It clearly clarifies the differences between automation and
intelligentization, including artificial intelligence based on algorithm analysis such as machine
vision and deep learning. Technology has become a new trend for the future development of
Industry 4.0. Not only does automation and robotics become more precise, but manufacturing
has also begun to enter new technological fields such as unmanned factories.
In terms of current development, there are three major trends in intelligent manufacturing. The
first is the production network. This part mainly applies Manufacturing Operations Management
(MOM) to assist suppliers in the production value chain to obtain and exchange real-time
production information. All components provided by the supplier can reach the production line
at the right time and in the correct order. The second trend is the perfect integration of virtual
simulation and real physical systems. Each step in the manufacturing process will be in the virtual
world. Designed, simulated and optimized to build a highly simulated Digital Twin (Twin Model)

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for the real physical world including materials, products, factories, etc. The third trend is the
Cyber-Physical System, CPS). In this system, the product information will be entered into the
product components themselves. They will directly communicate with the production system and
equipment according to their own production needs, issue the next production process
instruction, and direct the equipment to organize production. An independent production mode
can meet the customized needs of each user.
Building a computing model with big data
The above three major trends will be integrated with AI to a certain extent in the future. For
example, in the production line monitoring, robots, unmanned trucks, etc., there will be AI
computing function design. The main reason is that a large number of customized trends,
factories need to face Difficulties in various production scenarios such as product types and
production line transfers will also increase greatly. Although sensors and big data analysis can
help managers to grasp more information to help decision-making, it is also due to the large
increase in the amount of information. , Increase the pressure of manager's information analysis,
and the market is changing faster and faster, I am afraid that human analysis speed has become
more and more difficult to keep up with the provision of faster and faster front-end data, it is
naturally more difficult to make the machine at the manufacturing site Taiwan can quickly
respond to customer needs. AI applied to manufacture will allow the system to find regular
establishment patterns from big data analysis, and then learn to avoid previous errors, and even
predict in advance, applied to the manufacturing field, which can not only shorten The downtime
can also be adjusted in time to reduce the frequency of dull and waste materials.
For the Industrial Internet of Things, obtaining and analyzing data is a core task, and data points
from sensors can be transformed into actionable insights through multiple stages. The Industrial
Internet of Things platform includes scalable data processing processes that can handle the need
for immediate Real-time data that is of interest and data that is meaningful only for a period of
time. After detecting the abnormal combination of pressure and temperature thresholds, it may
be too late for the IoT platform to shut down the LPG filling machine, and it should be detected
within milliseconds To the exception, and then trigger an immediate response in accordance with
the rules.
In terms of current development, AI has several algorithms. For example, the core of hotspot
path analysis is the rule engine responsible for detecting abnormalities. The IoT platform embeds
a complex rule engine that can dynamically evaluate complex patterns from sensor data streams.
Data format experts define the benchmark threshold and routing logic of the rule engine. This
logic serves as the key input for the rules engine in orchestrating the message flow and is defined
for each data point before the data point moves to the next stage of the data processing flow.
Nested statement conditions, the rule engine has become the core of IoT platforms, and one of
the key areas of machine learning is to find patterns from existing data sets, group similar data
points, and predict the value of future data points.
High-level algorithms related to machine learning can be used for classification and predictive
analysis. Since these algorithms can learn from existing data, and most IoT data is based on time
series, these algorithms can predict the future value of sensors based on historical data. The
combination of these multiple machine learning algorithms will replace the traditional rule

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engine in the Industrial Internet of Things platform. Although domain experts still need to take
action based on conditional definitions, these intelligent algorithms provide higher accuracy and
precision.
AI + HI significantly improves benefits
One of the biggest applications of machine learning in the Industrial Internet of Things is the
predictive maintenance of equipment. It predicts equipment failures through correlations and
analysis mode changes and reports key indicators such as the remaining life of the equipment.
Predictive maintenance can also be applied in the future. Aerospace, manufacturing, automotive,
transportation, logistics, and supply chain fields, such as predictive models are arranged to car
service centers. In the aviation industry, the goal of predictive maintenance solutions is to predict
flight delays based on relevant data such as maintenance history and flight route information. Or
the possibility of cancellation.
Observe the development trend of the Internet of Things. Currently, the Industrial Internet of
Things is one of the fastest-growing categories in all vertical applications. AI in the Industrial
Internet of Things mainly assists operators and managers to screen data extracted from a large
number of devices and do Judgment, but the current AI cannot make logical decisions. Therefore,
in the field of manufacturing, AI must be combined with human wisdom to be the best benefit of
the system.