Professor Jay Lee graduated from George Washington University in the United States with a doctoral degree in 1992. I am currently a professor at the University of Cincinnati and the director of the National Science Foundation's (NSF) Intelligent Maintenance System (IMS) Industry University Collaboration Center. My current research focus is on the master controlled innovative design of intelligent prediction technologies, products, and services primarily focused on industrial big data analysis. Professor Li Jie has been serving as an advisor to the White House Information Physics Systems (CPS) expert group since 2013. He is also a distinguished professor at Shanghai Jiao Tong University and a prospective advisor to the Advanced Industrial Technology Research Institute.

Since 1992, Professor Li Jie has broken through the traditional theories, methods, and techniques of fault diagnosis and established the concept and basic theory of mechanical equipment performance decline. We have established a theoretical and methodological system for evaluating, predicting, and making decisions on performance degradation of the system, laying a technical foundation for achieving "predictive intelligent maintenance" of products and equipment. In 2000, the first intelligent maintenance system industry university cooperation center in the United States was established, based on the University of Wisconsin and the University of Michigan, to conduct alliance research with 40 major companies worldwide. Professor Li Jie has published over 100 papers in the fields of robotics and automation, as well as machine performance diagnosis. He has edited 3 books and participated in 9 publications. He has led and completed over 40 scientific research projects and three US patents. For many years, we have been committed to advocating for the proposed theory of Intelligent Machine Degradation Prediction, and advocating and promoting international industry university cooperation research and education between the business community and universities. The criticality of its technology has been recognized and highly praised by the international academic community, and is hailed as one of the three popular technologies in the world in the 21st century by the American magazine Caifu.

At the same time, Professor Li Jie is also a member of the American Society of Mechanical Engineering (ASME) and the American Society of Manufacturing Engineering (SME). I have previously led advanced manufacturing projects at the National Science Foundation in the United States and served as the Director of Product Development and Manufacturing at the United Technology Research Center (UTRC) in the United States. I have led research projects on automation, materials, green processes, and manufacturing technology in next-generation products such as Pratt&Whitney engines, Sikorsky helicopters, Carrier air conditioners, and Otis elevators. Professor Li Jie has successively served as a senior consultant in national research institutions such as the National Academy of Engineering of the United States, the Advanced Industrial Technology Research Institute of Japan, and Alstom of France. Since 1993, Professor Li Jie has participated as an American expert in the first to eighth China US Engineering Science and Technology Seminars, and has served as the head of the mechanical engineering team several times. He has been received by leaders such as Jiang Zemin and Zhu Rongji.

Let's take a section of a speech and get to know this outstanding professor and "Industry 4.0".

Samsung, high-speed rail, GE, and 80 other companies from 15 countries and regions have been cooperating with us since 2000. US President Obama announced 3D printing in 2001, and earlier this year announced three manufacturing projects, including low energy semiconductors, intelligent manufacturing, and lightweight material vehicle bodies. We participated in the third project, working with the University of Michigan, Siemens, and GE to develop a big data system for predicting the future of this manufacturing type.

What is invisible? The global manufacturing landscape is changing now, and it seems that China is now second place, but Southeast Asia is also growing, not just in Asia, so Singapore's position is very important. We are looking at the six S strategies of the United States - aerospace, semiconductor, shale gas, intelligent service economy, Silicon Valley, and sustainable human resources. Many countries can spell first, second, and third, but it is difficult for you to spell fourth, fifth, and sixth because the United States is only made up of one person.

Looking at important countries, China has a large number of university students due to its Ministry of Education, but there are not enough scientists studying engineering. So there are still not enough talents in China in 2020, and if you don't have enough engineering talents, innovation will always be behind you. So, our current competition is visible competition and problem-solving, but in the future, competition will be invisible. Competition that cannot be seen relies entirely on data analysis, because data analysis speaks and data cannot speak. For example, a machine tool's data cannot speak, and after analysis, its tool wear will speak.

There are two problems with manufacturing: visible issues and invisible issues, which are different strategies. What China is doing now is all visible problems. With the iPhone, Samsung and Xiaomi, they are the same. They are cheaper. But how do you create value inside? For example, this is a insole machine that can tell you the weight and pressure immediately after standing up for 5 seconds. Choose a insole that costs 50 US dollars or 300 RMB. Why? I think you all want to buy a pair for your parents tonight because it will protect your joints from wear and tear. The materials have not changed, but when you change their meaning, their value changes.

We use egg yolk and protein as examples. Apple phones have visible egg yolks, but what is not visible inside is all APPS, and you cannot copy everything. If you want to sell a machine tool for a small amount of money, productivity is a big deal. An engine sold to an airline keeps flying, and if big data shows it, eventually a protein enterprise is created, which is the value creation of the airline. Of course, there are still many air traffic controllers in China.

Big data has 6 Cs, the first one is the Internet of Things, and your phone is the Internet of Things. Cloud computing is rarely used in real mobile phones. Virtual network, when we look in the mirror, we need to know the good and bad places. If the button is not fastened, we can see it, but the product does not have a mirror and cannot be seen. The content and significance of information, and finally, sharing and customization.

The addition of the Internet of Things is related to its value and quality. There is a lot of data coming in from big data, and there are many software outside, such as SAP outside and ERP inside. However, there is a transformation in the middle, where data can be directly transmitted from data to SAP, telling them whether they are healthy or not. This SAP cannot do it.

Industrial big data refers to analyzing these unseen problems, and then using data conditions to identify problems that have not yet occurred, in order to avoid them. On this basis, repair before problems arise, rather than after asking questions.

Let's talk about big data today. Two years ago, GE of Germany established the industrial internet of things system to analyze something that can be understood by individuals. The GE website and our IMS website were basically the same 5 years ago. Why did GE continue to do it after 2 years? For GE, if a market saves 1% and I have 15000 engines, I can save $30 billion over 15 years. This 1% is your benefit.