Industrial AI (eBook)

Applications with Sustainable Performance

Jay Lee (Autor)

eBook Download: PDF

2020
XX, 162 Seiten
Springer Singapore (Verlag)
978-981-15-2144-7 (ISBN)

Infos

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.

Prof. Jay Lee is Ohio Eminent Scholar, L.W. Scott Alter Chair Professor, and Univ. Distinguished Univ. Professor at the Univ. of Cincinnati and is founding director of National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) on Intelligent Maintenance Systems (IMS) (www.imscenter.net) which consists of the Univ. of Cincinnati (lead institution), the Univ. of Michigan, Missouri Univ. of S&T, and the Univ. of Texas-Austin. Since its inception in 2001, the Center has been supported by over 100 global companies. IMS was
selected as the most economically impactful I/UCRC in the NSF Economic Impact Study Report in 2012 which reported that the Center has delivered to its members a combined benefit of $847.6 million in cost savings, and that the Center returned $238.30 of benefits for every $1 invested by the National Science Foundation. He was selected to be one of the 30 Visionaries in Smart Manufacturing in U.S. by SME in Jan. 2016. In addition, he is co-Founder of Predictronics (a start-up company through NSF ICorp award in 2012). In addition, his Team has won the 1st Place PHM Data Challenges five time out of nine competitions since 2008.

Dr. Jay Lee also serves as a board member and vice chairman of Hon Hai Precision (Foxconn) to support Foxconn investment in the establishment of Advanced Manufacturing Science Park (WisconnValley) in WI. In addition, he serves as a senior advisor to McKinsey & Company, Member of the Global Future Council of World Economic Forum (WEF), member of Board of Governors of the Manufacturing Executive Leadership Board of Frost Sullivan, etc.
Previously, he served as director for product development and manufacturing at United Technologies Research Center (UTRC) as well as program directors for a number of programs at NSF including the Engineering Research Centers Program, the Industry/University Cooperative Research Centers Program, and Materials Processing, and Manufacturing Program, etc., etc. He also served on the Board on National Research Council (NRC) Manufacturing and Engineering Design (BMAED) during 1999-2005 as well as a number of NRC Study and Assessment Panels since 1999. He is a frequently invited speaker and has delivered over 260 keynote and plenary speeches at major international conferences. He is a Fellow of ASME, SME, PHM (Prognostics and Health Management), as well as a founding fellow of International Society of Engineering Asset Management (ISEAM).

He has received a number of awards including the Prognostics Innovation Award at NI Week by National Instruments in 2012, NSF Alex Schwarzkopf Technological Innovation Prize in 2014, MFPT (Machinery Failure Prevention Technology Society) Jack Frarey Award in 2014, and PICMET Medal of Excellence in 2016.

Inhalt

Foreword by Terry Gou 5
Foreword by Andrew James Hess 8
Foreword by Detlef Zuehlke 10
Foreword by Yasushi Umeda 12
Preface 13
Acknowledgements 15
Contents 16
1 Introduction: The Development and Application of AI Technology 18
2 Why Do We Need Industrial AI? 22
2.1 New Perspectives in Industrial Systems for AI 22
2.2 What Are the Basic Problems in Industry? 24
2.3 The Basic Method of Solving Problems with AI 27
2.4 What Kind of AI Technology Is Most Suitable for Industry? 29
2.4.1 Neural Networks: The Closest to Thinking, and Close to Solving Complex Problems 29
2.4.2 Statistical Method: A Summarization of the Experience 32
2.4.3 Cybernetics Approach: Systematic Design Perspectives with an Emphasis on Objects and Tasks 33
2.4.4 Industrial AI Isn’t Just Algorithms, But the Integration of People, Things, and Systems 36
2.5 When Machine Intelligence Meets Industry 37
2.6 Differences Between Industrial AI and AI 39
2.7 Challenges of AI in Industry 42
2.7.1 Reproducibility 42
2.7.2 Data Issues 43
2.7.3 Reliability 44
2.7.4 Safety/Security 45
2.8 New Opportunity Spaces for Industrial AI to Realize Industrial Value Transformation 45
References 48
3 Definition and Meaning of Industrial AI 50
3.1 The Beginnings of Industrial AI 50
3.2 The Purpose and Value of Industrial AI 56
3.3 GE Predix Successes and Failures 61
3.4 Technical Elements of Industrial AI: Data, Analytics, Platform, Operations, and Human-Machine Technologies 65
3.5 CPS: An Architecture for Integrating the 5 Technological Elements of Industrial Intelligence 69
3.6 Industrial AI: Categories of Algorithms 71
3.6.1 Regression Algorithms 73
3.6.2 Classification Algorithms 73
3.6.3 Clustering Algorithms 74
3.6.4 Statistical Estimation Algorithms 74
3.7 Industrial AI Algorithms: Selection and Application 75
References 78
4 Killer Applications of Industrial AI 79
4.1 Application Scenario Types for Industrial AI 79
4.2 What Will Become the “Killer Applications” of Industrial AI? 81
4.2.1 Predictive Maintenance of Equipment 81
4.2.2 Virtual Metrology and Process Quality Control 90
4.2.3 Energy Management and Energy Efficiency Optimization 97
4.2.4 Defect Detection and Material Sorting Based on Machine Vision 106
4.2.5 Scheduling Optimization of Production and Maintenance Plans 110
4.3 Enabling Industrial AI Systems 118
4.3.1 Intelligent Monitoring and Maintenance Platform for CNC Machines 118
4.3.2 Intelligent Operations and Maintenance System for Offshore Wind Farms 122
4.3.3 Intelligent Rail Transit Predictive Maintenance System 128
References 132
5 How to Establish Industrial AI Technology and Capability 134
5.1 Assessment of Basic Capability Maturity During Industrial Intelligence Transformation 134
5.2 Assessment Tools for Global Industrial AI Enterprise Transformation Achievements 139
5.3 Foxconn Lighthouse Factory 144
5.4 How to Construct the Organizational Intelligent Transformation Ability in Industrial Enterprises 148
5.5 Open Source Industrial Big Data Competitions 152
References 173
6 Conclusion 175

Produkt-Details

• Erscheint lt. Verlag: 7.2.2020
• Zusatzinfo: XX, 162 p. 106 illus., 98 illus. in color.
• Sprache: englisch
• Themenwelt: Sachbuch/Ratgeber ► Natur / Technik
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Unternehmensführung / Management
• Schlagworte: 4th Industry revolution • Artificial Intelligence • cps • Cyber-Physical Systems • Industrial AI • Industrial Big Data • Intelligence Manufacturing • PHM • Predictive Maintenance
• ISBN-10: 981-15-2144-1 / 9811521441
• ISBN-13: 978-981-15-2144-7 / 9789811521447