Description
hardware flow control. It is an ideal choice in the field of industrial automation.
Although it was established only a few months ago, ABB Future Lab has already cooperated with Huawei to
complete the AI training of garbage sorting robots in two months. This robot uses AI technology based on Huawei
chips and uses industrial cameras to sort objects.
Obviously, adjusting the business model and organizational structure is an important reason why ABB has
lways been at the forefront of the industry.
From technology iteration to application implementation, ABB aims to provide complete solutions
As a pioneer in manufacturing automation and digitalization, ABB”s products in the automation field are constantly
improving, and its application industries are also expanding.
In Alf”s view, the structure and construction of automation have not changed much in the past 30 years. From signal
collection and information transmission to work scenarios or operational interfaces, the flow of information in automated systems has basically not changed.
But the arrival of 5G may change the way information is transmitted between devices. 5G’s characteristics such as large
capacity, high reliability and low latency make it possible to realize independent connections between devices. If real-time
mainline connection can be achieved and installed on the cloud or platform, it will be a more disruptive application.
5G will not only change the way ABB operates, but is also likely to introduce digitalization into the industry, including digital
operation and maintenance. ABB has launched an industrial Internet solution called ABB Ability, which firstly provides a platform
, and secondly, it includes all ABB”s digital cloud products, such as equipment industrial solutions and transportation solutions.
In addition to digitization, another focus of Alf is the autonomy of machines. He took the autonomous management of
autonomous driving and warehousing as examples to think about the development process of machine autonomy –
from human operation to machine operation, and ultimately to autonomous implementation and testing without the need for humans at all.
For ABB, in addition to popular autonomous driving technology, industrial production scenarios also have strong
demand for the autonomy of machinery and equipment, such as autonomous docking of ships and the mining industry
. In some industrial scenarios represented by mining, toxic gases and substances in the working environment are
harmful to the human body, so equipment
is required to have the ability to enter and leave the mine independently. ABB first needs to study the value proposition
of these businesses, discover potential applications, and then discover in which fields it can be applied.
A technology close to autonomy is artificial intelligence. Since its birth in the 1960s, it has been attracting people”s
attention, and there have been endless discussions about “robot replacement”. After decades of development,
related technologies have gradually matured, and more and more AI technologies have begun to be discussed
in the application field. The products and technologies
of leading manufacturers such as ABB have attracted much attention.
ABB has been applying AI technology to its products for 20 years, but its current mature products are mainly
diagnostic applications based on traditional statistics. Alf introduced to Yiou New Manufacturing that
as part of the diagnostic solution, this technology is mainly used to implement condition monitoring functions
. More mature applications are reflected in the automated management of equipment, such as the electronic
management of ships.
Machine learning is another promising AI technology. At the World Artificial Intelligence Conference at the
end of August this year, ABB demonstrated a coffee workbench composed of a two-arm collaborative robot Yu Mi.
Through guided programming of YuMi, the collaborative robot can learn and
remember the barista”s movements to complete the complete process of making coffee, latte art and delivering it to the audience.
In actual implementation scenarios, this technology is used to transform terminals carrying containers. By simulating the
location and status of box handling, the collected data is used to train the AI, allowing the AI to know the
location of the container, thereby achieving automation.
From automation, digitalization, autonomy and artificial intelligence technologies, ABB is not a blind pursuer of
emerging technology concepts. It pays more attention to customer needs and actual implementation, and achieves
better solutions through different product combinations.
There are two major difficulties in balancing R&D and business and implementing solutions.
As a commercial company, ABB still needs to consider the balance between R&D investment and revenue.
In Alf”s view, they do not have unlimited R&D resources, so they need to focus on finding a balance between
improving original product functions and developing new functions. In terms of technology research and development,
we also need to try to focus on projects that can bring the greatest value to the company.
Regarding the specific implementation of digital solutions, Alf believes that there are currently two main difficulties.
The first difficulty is that ABB cannot just develop a general solution, because different industry segments
have different needs, so it must design solutions that suit their different needs. ABB not only needs to master
knowledge in different fields and different applications, but also needs to consider the availability of data.
The second difficulty lies in the use of data, because AI requires a large amount of data training. On
the one hand, ABB needs to encourage customers to provide data for training models; on the other hand,
ABB also needs to ensure customers the privacy, ownership and security of their data.
“Industrial artificial intelligence needs to be combined with models and data. But the most
important thing is that we must provide value to customers through the use of AI, otherwise we will just
apply technology for the sake of applying technology.” Alf concluded.
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DS200TCTLG1A driver board
DS200TCTGG2A robot power supply board
DS200TCTGG1AFF analog output module
DS200TCTGG1AEC digital output module
DS200TCTGG1A input module
DS200TCTEG1ABA communication module
DS200TCTEG1A I/O processor
DS200TCSAG1A sensor jamming card
DS200TCRAG2A relay output card
DS200TCRAG1ACC Turbine output module
DS200TCRAG1A relay output
Ge DS200TCQFG1B Mark V circuit board
DS200TCQFG1ACC electrical simulation extension module
DS200TCQFG1ACB analog expansion card
DS200TCQFG1A analog I/O expansion card
DS200TCQEG2AED Processor Mark V board
DS200TCQEG2A input/output I/O processor
DS200TCQEG1A input/output processor
DS200TCQCG1BKG General Electric RST overflow plate
DS200TCQCG1BJF RST board
DS200TCQCG1BHF overflow RST PCB
DS200TCQCG1BBA General Electric RST overflow plate
DS200TCQCG1B overflows the RST board
DS200TCQCG1AHE General Electric input/output module
DS200TCQCG1AHD GE Mark V module
DS200TCQCG1ADB simulation card
DS200TCQCG1A turbine
DS200TCQBG2AEB extended analog I/O board
DS200TCQBG2A extended analog board
DS200TCQBG1BBA overflow card
DS200TCQBG1B emulates the RST expansion board
DS200TCQBG1AFB extended analog I/O
DS200TCQBF1BAD turbine control system
DS200TCQAG2B analog I/O card
DS200TCQAG1BHF input/output turbine control
DS200TCQAG1BGE Analog I/O input board
DS200TCQAG1BEC PC board
DS200TCQAG1BDC driver board
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