New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques: Advanced Machine Learning Models, Methods and Applications (新一代人工智慧驅動的診斷與維護技術：先進機器學習模型、方法與應用)
Wen, Guangrui, Lei, Zihao, Chen, Xuefeng
- 出版商: Springer
- 出版日期: 2024-09-29
- 售價: $7,150
- 貴賓價: 9.5 折 $6,793
- 語言: 英文
- 頁數: 349
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9819711754
- ISBN-13: 9789819711758
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相關分類:
人工智慧、Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
The intelligent diagnosis and maintenance of the machine mainly includes condition monitoring, fault diagnosis, performance degradation assessment and remaining useful life prediction, which plays an important role in protecting people's lives and property. In actual engineering scenarios, machine users always hope to use an automatic method to shorten the maintenance cycle and improve the accuracy of fault diagnosis and prognosis. In the past decade, Artificial Intelligence applications have flourished in many different fields, which also provide powerful tools for intelligent diagnosis and maintenance.
This book highlights the latest advances and trends in new generation artificial intelligence-driven techniques, including knowledge-driven deep learning, transfer learning, adversarial learning, complex network, graph neural network and multi-source information fusion, for diagnosis and maintenance of rotating machinery. Its primary focus is on the utilization of advanced artificial intelligence techniques to monitor, diagnose, and perform predictive maintenance of critical structures and machines, such as aero-engine, gas turbines, wind turbines, and machine tools.
The main markets of this book include academic and industrial fields, such as academic institutions, libraries of university, industrial research center. This book is essential reading for faculty members of university, graduate students, and industry professionals in the fields of diagnosis and maintenance.
商品描述(中文翻譯)
智能診斷與維護機器主要包括狀態監測、故障診斷、性能衰退評估和剩餘使用壽命預測，這在保護人們的生命和財產方面扮演著重要角色。在實際工程場景中，機器使用者總是希望能夠使用自動化的方法來縮短維護週期，並提高故障診斷和預測的準確性。在過去十年中，人工智慧應用在許多不同領域蓬勃發展，這也為智能診斷和維護提供了強大的工具。
本書突顯了新一代人工智慧驅動技術的最新進展和趨勢，包括知識驅動的深度學習、轉移學習、對抗學習、複雜網絡、圖神經網絡和多源信息融合，應用於旋轉機械的診斷和維護。其主要重點在於利用先進的人工智慧技術來監測、診斷和執行關鍵結構和機器的預測性維護，例如航空發動機、燃氣渦輪機、風力渦輪機和機床。
本書的主要市場包括學術和工業領域，如學術機構、大學圖書館、工業研究中心。本書是大學教職員、研究生以及診斷和維護領域的行業專業人士必讀的資料。
作者簡介
Guangrui Wen received his B.S., M.S., and Ph.D. degrees from the School of Mechanical Engineering, Xi'an Jiaotong University (XJTU), China, in 1998, 2001, and 2006, respectively. From 2008 to 2010, he worked as a Postdoctoral Research Fellow at Xi'an Shaangu Power Co., Ltd., Xi'an. He was a visiting scholar of the University of Liverpool from 2017 to 2018. He has over 20 years of teaching and research experiences at XJTU.
Dr. Guangrui Wen is currently a Full Professor of the School of Mechanical Engineering and the Dean of the School of International Education in XJTU, China. He is also the Vice Dean of the Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, and the Vice Dean of National & Local Joint Engineering Research Center for Equipment Operation Safety Assurance and Intelligent Monitoring, China. Dr. Wen is a member of IEEE, the Chinese Mechanical Engineering Society, the Chinese Society for Vibration Engineering (CSVE), and the Executive Director and Deputy Secretary-General of the Dynamic Test Professional Committee of CSVE.
Dr. Wen has authored two books and over 130 peer-reviewed journal articles and holds more than 20 patents. Some of his work was published in top journals such as Information Fusion, Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Informatics and IEEE Transactions on Industrial Electronics. His research interests include artificial intelligence, mechanical system fault diagnosis and prognosis, mechanical equipment life cycle health monitoring and intelligent maintenance.
Dr. Wen won National Science and Technology Innovation Leading Talents of China in 2022, Young and Middle-aged Scientific and Technological Innovation Leading Talents of Shaanxi Province in 2021, the Third Provincial Science and Technology Award in 2019, the First Provincial Science and Technology Award and the Science & Technology Award for Shaanxi Youth Award in 2015, New Century Excellent Talents in University Award from the Ministry of Education, China, and the Science & Technology Award Achievement Award for Youths from CIME in 2013 and the Second Provincial Science and Technology Award in 2012. Dr. Wen is in charge of a National Science and Technology Major Project of China as the chief scientist from 2020 to 2024.
Zihao Lei received the B.Sc. degree in mechanical engineering from Southwest Jiaotong University, Chengdu, China, in 2018, and the Ph.D. degree in mechanical engineering from Xi'an Jiaotong University, Xi'an, China, in 2024. From 2022 to 2023, he was a visiting scholar in electrical engineering with the School of Engineering, the University of British Columbia, Canada. He worked at the Intelligent Sensing, Diagnostics, and Prognostics Research Lab of UBC as a research assistant from 2022 to 2023.
His current research focuses on new-generation artificial intelligence-driven diagnosis and maintenance techniques, such as deep learning, transfer learning, adversarial learning, graph neural networks, and information fusion.
He has authored/co-authored more than twenty papers in top journals, including Information Fusion, IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing, and Expert Systems with Applications.He reviews many manuscripts for over ten SCI journals such as Mechanical Systems and Signal Processing, IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man and Cybernetics: Systems, IEEE Transactions on Cybernetics, Expert Systems with Applications, ISA Transactions, IEEE Access, and Measurement Science and Technology. He has also participated in some research projects, including the National Key Research and Development Program, the National Science and Technology Major Project, and the National Science Foundation of China, and the Innovation for Defence Excellence and Security (IDEaS) program of Canada.
Xuefeng Chen is a Full Professor and Dean of the School of Mechanical Engineering at XJTU, China, where he received his Ph.D. degree in Mechanical Engineering in 2004. He is the executive director of the Fault Diagnosis Branch in China Mechanical Engineering Society, a member of ASME and IEEE, and the chair of IEEE the Xi'an and Chengdu Joint Section Instrumentation and Measurement Society Chapter.
His fields of interest include fault diagnosis, sparse representation, deep learning, composite structure, aero-engine and wind power equipment. He has authored over 100 SCI publications, 10 of which are Highly Cited Papers in fault diagnosis. He has also published two monographs and two postgraduate textbooks. He won the National Excellent Doctoral Thesis Award in 2007, the First Technological Invention Award of Ministry of Education in 2008, the Second National Technological Invention Award in 2009, the First Provincial Teaching Achievement Award in 2013, and the First Technological Invention Award of Ministry of Education in 2015. He received the National Science Fund for Distinguished Young Scholars in 2012 and the Science & Technology Award for Chinese Youth in 2013. He was in charge of a National Key 973 Research Program of China as the chief scientist in 2015.
Xin Huang received his B.S. and M.S. degrees in Mechanical Engineering from Xinjiang University, Urumqi, China, in 2013 and 2016, and his Ph.D. degree in Mechanical Engineering at the School of Mechanical Engineering at XJTU, Xi'an, China, in 2022. Currently, he works at the SINOPEC Research Institute of Safety Engineering Co., Ltd, Qingdao, China.
His research interests include mechanical signal processing, mechanical fault diagnosis, and prognosis.
作者簡介(中文翻譯)
郭瑞文（Guangrui Wen）於1998年、2001年和2006年分別獲得中國西安交通大學（Xi'an Jiaotong University, XJTU）機械工程學院的學士、碩士和博士學位。2008年至2010年，他在西安陝鼓動力有限公司擔任博士後研究員。2017年至2018年，他曾是利物浦大學的訪問學者。他在西安交通大學擁有超過20年的教學和研究經驗。
郭瑞文博士目前是西安交通大學機械工程學院的正教授及國際教育學院院長。他同時擔任教育部現代設計與轉子軸承系統重點實驗室的副院長，以及國家與地方聯合工程研究中心設備運行安全保障與智能監測的副院長。郭博士是IEEE、中國機械工程學會、中國振動工程學會（CSVE）的成員，並擔任CSVE動態測試專業委員會的執行董事和副秘書長。
郭博士已出版兩本書籍和超過130篇經過同行評審的期刊文章，並擁有20多項專利。他的一些研究成果發表在《Information Fusion》、《Mechanical Systems and Signal Processing》、《IEEE Transactions on Industrial Informatics》和《IEEE Transactions on Industrial Electronics》等頂尖期刊上。他的研究興趣包括人工智慧、機械系統故障診斷與預測、機械設備生命週期健康監測及智能維護。
郭博士於2022年獲得中國國家科技創新領軍人才，2021年獲得陝西省青年及中年科技創新領軍人才，2019年獲得第三屆省級科技獎，2015年獲得第一屆省級科技獎及陝西青年科技獎，並於2013年獲得教育部新世紀優秀人才獎和2012年第二屆省級科技獎。郭博士自2020年至2024年擔任中國國家科技重大專案的首席科學家。
雷子豪（Zihao Lei）於2018年獲得中國成都西南交通大學的機械工程學士學位，並於2024年獲得中國西安交通大學的機械工程博士學位。2022年至2023年，他在加拿大不列顛哥倫比亞大學工程學院擔任電機工程的訪問學者。他於2022年至2023年在不列顛哥倫比亞大學的智能感知、診斷與預測研究實驗室擔任研究助理。
他目前的研究重點是新一代人工智慧驅動的診斷與維護技術，如深度學習、轉移學習、對抗學習、圖神經網絡和信息融合。
他已在多個頂尖期刊上發表或共同發表超過二十篇論文，包括《Information Fusion》、《IEEE Transactions on Industrial Electronics》、《Mechanical Systems and Signal Processing》和《Expert Systems with Applications》。他為超過十本SCI期刊如《Mechanical Systems and Signal Processing》、《IEEE Transactions on Industrial Electronics》、《IEEE Transactions on Systems, Man and Cybernetics: Systems》、《IEEE Transactions on Cybernetics》、《Expert Systems with Applications》、《ISA Transactions》、《IEEE Access》和《Measurement Science and Technology》審稿。他還參與了一些研究專案，包括國家重點研究與發展計畫、國家科技重大專案、中國國家自然科學基金，以及加拿大的國防卓越與安全創新計畫（IDEaS）。
陳學峰（Xuefeng Chen）是正教授。
