In the field of equipment/product operation and maintenance (O&M) services, the new generation of information technologies such as the internet, big data, and artificial intelligence are deeply integrated with O&M services to form an internet-based Maintenance Repair & Operation (MRO) service network and an intelligent service environment. To deal with the uncertainties of multiple collaborative entities and highly random equipment failures in the large-scale MRO network, this book establishes the theory, technology, and methods of Intelligent Predictive Maintenance (IPd M) for the MRO service network through the study of high-quality acquisition and integration of multi-source heterogeneous data, data-driven equipment fault diagnosis and prediction, large-scale maintenance decision-making, feedback, and control. The book systematically elaborates on the emerging theories, technologies, and methods in the field of equipment/product O&M services, covering a wide range of topics with rich contents. It emphasizes both systematic and scientific approaches as well as practicality. It offers both comprehensive and specialized discussions to reflect the strategic deployment and implementation of China’s new generation of intelligent manufacturing and artificial intelligence in this field.
The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.
İçerik tablosu
Chapter 1 Introduction.- Chapter 2 Methods of Fault Diagnosis and Prediction.- Chapter 3 Intelligent Predictive Maintenance System and Framework.- Chapter 4 Io T-based Perception Resource Management Framework and Model.- Chapter 5 Wireless Routing Model and Algorithm for Complex Manufacturing Environment.- Chapter 6 Protocol Integration and Design Case of Data Collection.- Chapter 7 Data-driven Fault Diagnosis Methods.- Chapter 8 Data-driven Fault Prediction Model and Methods.- Chapter 9 Maintenance Optimization Scheduling and Decision Making in Intelligent Factories.- Chapter 10 Large-scale Maintenance Service Forecasting and Optimization Configuration.- Chapter 11 Operation Process Control based on Cyber-Physical Systems.
Yazar hakkında
Min Liu received the B.Sc. degree from China University of Geosciences, Wuhan, China, in 1993, and the M.Sc. and Ph.D. degrees from Zhejiang University, Hangzhou, China, in 1996 and 1999, respectively. He is a full professor at the College of Electronic and Information Engineering, Tongji University, Shanghai, China. He was a Post-Doctoral Fellow at the Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China, from 1999 to 2001. From 2001 to 2004, he was a product architecture engineer at Asia-Bridge Software Company Ltd, where he developed an enterprise resource management system. He has authored or co-authored over 100 papers in scientific journals and international conferences in related fields. He has been involved in research on artificial intelligence technologies for collaborative intelligent manufacturing and MRO services over 20 years. Ling Li received her bachelor’s and master’s degrees from Hubei University, Wuhan, China, in 2004 and 2007, respectively, and her Ph.D. in Control Science and Engineering from Tongji University, Shanghai, China, in 2018. Now she is a full professor at the School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China. Her research interests include service-oriented computing, supply chain management, intelligent maintenance and reliability theory. Feng Yan received his B.S., M.S. and Ph.D. degrees from Central South University, Changsha, China in 2003, 2007 and 2015, respectively. He is a senior engineer with the rank of a professor from CINF Engineering Co., Ltd. and serves as deputy director of the Science & Technology Management Dept. and R&D Centre of CINF. He has been honoured as an outstanding young engineer by the Nonferrous Metals Society of China and an outstanding automation engineer by the Chinese Association of Automation. He has been involved in the research and engineering application of automation and intelligent technology in the process industry over 15 years.
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