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Titlebook: Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems; Weihua Li,Xiaoli Zhang,Ruqiang Yan Book 2023 Nat

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書目名稱Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems
編輯Weihua Li,Xiaoli Zhang,Ruqiang Yan
視頻videohttp://file.papertrans.cn/470/469666/469666.mp4
概述Presents advanced machine learning paradigms for complex electro-mechanical system fault diagnosis and health assessment.Covers a wide range of research directions in intelligent fault diagnosis and h
圖書封面Titlebook: Intelligent Fault Diagnosis and Health Assessment for Complex Electro-Mechanical Systems;  Weihua Li,Xiaoli Zhang,Ruqiang Yan Book 2023 Nat
描述Based on AI and machine learning, this book systematically presents the theories and methods for complex electro-mechanical system fault prognosis, intelligent diagnosis, and health state assessment in modern industry. The book emphasizes feature extraction, incipient fault prediction, fault classification, and degradation assessment, which are based on supervised-, semi-supervised-, manifold-, and deep learning; machinery degradation state tracking and prognosis by phase space reconstruction; and complex electro-mechanical system reliability assessment and health maintenance based on running state info. These theories and methods are integrated with practical industrial applications, which can help the readers get into the field more smoothly and provide an important reference for their study, research, and engineering practice.
出版日期Book 2023
關鍵詞Intelligent Fault Diagnosis; Health Assessment; Complex Electro-mechanical System; Machine Learning; Art
版次1
doihttps://doi.org/10.1007/978-981-99-3537-6
isbn_softcover978-981-99-3539-0
isbn_ebook978-981-99-3537-6
copyrightNational Defense Industry Press 2023
The information of publication is updating

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Manifold Learning Based Intelligent Fault Diagnosis and Prognosis,is that aims to uncover the underlying structure or geometry of high-dimensional data in a lower-dimensional space. In mechanical field, faults often introduce subtle changes in the data patterns, making it difficult to identify them using traditional methods. Manifold learning?can help capture thes
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Deep Learning Based Machinery Fault Diagnosis,dictions from data. It has gained wide?attention and popularity due to its remarkable success in various complex tasks, such as image and speech recognition, natural language processing, and?game playing.?Due to its ability to automatically learn complex patterns and representations from data, deep
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