The giant behind the scenes is taking industrial AI to the next stage

Industrial AI, there is no new king, light but no glory, still water flows deep. No one would object to saying that generative AI is the king of topics today. With a few simple words, the Terracotta Warriors can be "resurrected" to sing Qin Opera, and Trump can appear on a talk show. When the emotional value is full, do you dare to imagine cooler things, such as being able to create what you want with just your words? AI can not only generate a video, but also build an immersive, highly simulated virtual space that follows physical laws. It only needs natural voice to input instructions, and it can convert them into professional industrial language, and then hand them over to the intelligence of the real factory. The chemical production line becomes a "real thing". Dare to imagine the cool things, you can create what you want!

Such a wonderful future may seem far away, but according to Siemens’ depiction, it is no longer a castle in the air. The application of AI in the industrial field is entering a new stage.

In April this year, Siemens demonstrated its world’s first generative AI product for industrial engineering design, Industrial Copilot. This tool has been launched on the production line of Schaeffler in Germany; at the just-concluded Achema exhibition, Siemens For the first time, a number of new software tools for the green hydrogen industry have been launched to increase hydrogen production through the application of generative AI; Siemens' first industrial time series data basic model is also being developed and trained, and will continue to be optimized and iterated based on Siemens's trillion-level data set in the future... …

Industrial artificial intelligence "drives" the factory

Imagine that you walk into the production workshop of Germany's Schaeffler, one of the world's largest automotive suppliers. Looking around, various automated equipment are operating in an orderly manner.

"I want to add a new graph block to the band and name it 210 sequence." An equipment operator opens the Siemens Industrial Copilot dialog box and enters the request in simple natural language. Soon, the virtual assistant answered, "I have added a S7-Graph block to 0210 (Band)."

Last year, Nuremberg, Germany's Nuremberg international electrical automation system and component exhibition (SPS). Language commands to automatically generate complex industrial codes.

At this time, another operator used natural language to guide the virtual assistant and asked the robotic arm of the production line to grab. The system called the corresponding module in the robot function library, and the robotic arm grabbed the items on the assembly line.

In addition to code generation and optimization, engineering teams at Schaeffler plants can use natural language to access relevant documentation, guides and manuals to quickly identify potential causes of errors and find solutions when equipment suddenly stops working.西 Last year's SPS, Siemens Show and Industrial Copilot Simple Dialogue can find the cause of the device failure.

Compared with consumer products that are full of experience, the interactive experience of enterprise-level software is still extremely complex, which also reduces product development efficiency. Siemens is the first to use generative artificial intelligence technology to reconstruct the industrial software experience, greatly improving the work efficiency of engineers. At the just-concluded 2024 World Artificial Intelligence Conference WAIC, Industrial Copilot also won the "SAIL (Super AI Leader, Outstanding Artificial Intelligence Leader Award) Star" award.

However, Siemens is not only satisfied with providing innovative artificial intelligence products like Industrial Copilot, it is also a user and practitioner of industrial artificial intelligence. In Siemens’ own factories, the organic combination of a large number of artificial intelligence technologies and scenarios has already become a breathing thing.

In Chengdu High-tech Zone, Siemens established its first digital factory in China. Walking into the workshop, you can hardly see many operators on the fully automated production line. There are only a few workers behind the production line, operating the mouse and keyboard and issuing instructions.

This "Lighthouse Factory" has deployed nearly 100 AI projects, which are used in multiple scenarios such as quality inspection and garbage disposal.

The production line is equipped with automatic optical inspection (AOI) equipment to detect the quality of circuit board solder joints, but the strict standard settings bring a large number of "false positives" and require a lot of manual re-inspection. By adding an AI system after the AOI equipment for secondary inspection, the factory successfully filtered out more than 90% of "quality-problematic" images, greatly reducing the workload of workers.

By adding an AI system after the AOI equipment for secondary inspection, the factory successfully filtered out more than 90% of "quality-problematic" pictures.

For factories, the disposal of industrial waste is a big trouble. The front-end production line runs 24 hours a day, and the factory generates thousands of boxes of industrial waste every day. Now, the AI ​​sorting robot's identification rate of hazardous waste reaches 100%, the identification rate of other materials such as finished products reaches 94%, and the comprehensive identification rate exceeds 96%. There is no need for manual garbage disposal.

Out of the "One-stop Wulin"

High-quality industrial data allows AI to release productivity

Roland Busch, the head of Siemens, once said that the existence of artificial intelligence technology alone is meaningless. Only when it is put into various industries can it produce huge benefits.

However, the quality and availability of industrial data have always restricted the current large-scale application of AI. In China's large number of industrial manufacturing sites, data types are complex and of varying quality. Only a large amount of high-quality industrial data can train reliable industrial models, and these qualified industrial "nutrients" are not collected in one step from collection to use. Easy.

With its huge market share, it has accumulated massive industrial data resources, which has become its core competitiveness in the digital era.

GTT 1.0, Siemens’ first time series data basic model that is being developed and trained, is a concentrated expression of this natural advantage.

These advantages are deeply rooted in Siemens’ comprehensive layout and deep integration in the fields of industrial hardware and software.

Siemens’ hardware product line is extremely extensive. Its industrial control system is the cornerstone of traditional enterprise digitalization, and controllers (PLCs) are used in one-third of the world's factories.

For example, the Siemens Industrial Edge edge computing platform can be deployed close to the data source to achieve data collection, processing and analysis.

Siemens is deeply involved in the formulation of communication protocols such as fieldbus and industrial Ethernet, enabling it to collect field-level data with extremely high granularity and accuracy.

Similarly, in the field of software, Siemens has also shown strong strength. Siemens has gradually built a comprehensive industrial software ecosystem, making the differences between different levels (such as equipment layer, workshop layer, enterprise layer), different systems (such as MES, PLM), and different departments (such as design, production, service) The integration and collaboration of type data becomes possible, forming Siemens’ unique data advantages.

This experience of "unity of knowledge and action", coupled with the comprehensive layout of "implementing both software and hardware", ultimately builds Siemens' unparalleled data ecological advantage.

Open up the two channels of Ren and Du

Full infusion of industry knowledge allows AI to learn to work

Since the complexity of industrial data far exceeds general understanding, in addition to common image and text data, the industrial field also includes things such as logic control, Time series data and data in multiple modalities such as various images and 3D models. Understanding and leveraging this data requires extensive industrial background and experience.

"If AI wants to move from consumption to industry, it must be deeply integrated with industrial scenarios, open up the connections between numbers and mechanisms, and achieve a leap in productivity with safe, reliable, and trustworthy industrial-grade AI," said Siemens Global Executive Vice President , Xiao Song, Chairman, President and CEO of Siemens China, said in his speech at the 2024 WAIC Industrial Development Plenary Meeting.

1. Xiao Song, Global Executive Vice President of Siemens, Chairman, President and CEO of Siemens China, spoke at the 2024 WAIC Industrial Development Plenary Meeting.

2. Industrial generative AI products like Industrial Copilot are produced by Siemens.
3. PLC programming languages ​​such as ladder diagram and instruction list are specially designed for industrial control and are inseparable from hardware knowledge.
4. Siemens provides a large amount of industry knowledge, practical application cases, and knowledge and rules in professional fields to build a rich training data set to ensure that the generated code meets industry standard requirements.
5. SiePA Xssistant is Siemens’ new “assistant” for its industrial predictive analytics software SiePA, which applies a generative artificial intelligence QRA framework tailored for industrial professional applications.
6. The successful implementation of industrial AI is inseparable from rich field experience and deep industry know-how.
7. Process engineers play a vital role in AI inspection model construction, result evaluation and interpretation.
8. More than 80% of Chinese manufacturing companies are eager for AI solution providers to provide full-process, end-to-end solutions from early consultation and planning to later implementation and operation and maintenance.
9. Siemens has accumulated a "century-old industrial heritage" (covering products and knowledge) and is able to provide a full range of services for industrial AI projects.
10. Siemens’ unremitting pursuit of technological innovation has been deeply integrated into its DNA.
11. Siemens began participating in artificial intelligence research as early as 1974.
12. Siemens has been working in the field of industrial AI for more than 50 years.
13. Today, Siemens has more than 1,500 AI experts in the field of industrial AI, and the number of AI patent applications has reached 3,700, ranking first in Europe.
14. A solid foundation for Siemens to explore the development path of industrial AI.
15. Success lies in capturing real market needs, building a sustainable ecosystem and business model, and creating real value for the industry.

The above is the detailed content of The giant behind the scenes is taking industrial AI to the next stage. For more information, please follow other related articles on the PHP Chinese website!