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Titlebook: Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems; Yaguo Lei,Naipeng Li,Xiang Li Book 2023 Xi‘a(chǎn)n Jiaotong U

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發(fā)表于 2025-3-21 18:30:51 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems
影響因子2023Yaguo Lei,Naipeng Li,Xiang Li
視頻videohttp://file.papertrans.cn/186/185734/185734.mp4
發(fā)行地址Provides basic theories and detailed background for fault diagnosis and prognosis.Covers state-of-the-art techniques and advancements in the field of intelligent fault diagnosis and RUL prediction.Pro
圖書封面Titlebook: Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems;  Yaguo Lei,Naipeng Li,Xiang Li Book 2023 Xi‘a(chǎn)n Jiaotong U
影響因子.This book presents systematic overviews and bright insights into big data-driven intelligent fault diagnosis and prognosis for mechanical systems. The recent research results on deep transfer learning-based fault diagnosis, data-model fusion remaining useful life (RUL) prediction, etc., are focused on in the book. The contents are valuable and interesting to attract academic researchers, practitioners, and students in the field of prognostics and health management (PHM). Essential guidelines are provided for readers to understand, explore, and implement the presented methodologies, which promote further development of PHM in the big data era...Features:..Addresses the critical challenges in the field of PHM at present.Presents both fundamental and cutting-edge research theories on intelligent fault diagnosis and prognosis.Provides abundant experimental validations and engineering cases of the presented methodologies.
Pindex Book 2023
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書目名稱Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems影響因子(影響力)




書目名稱Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems影響因子(影響力)學(xué)科排名




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書目名稱Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems網(wǎng)絡(luò)公開度學(xué)科排名




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發(fā)表于 2025-3-21 23:19:47 | 只看該作者
Conventional Intelligent Fault Diagnosis, at present, the typical neural network models are briefly reviewed, as well as their applications in the fault diagnosis problems for mechanical systems. The radial basis function networks and the wavelet neural networks are included. Next, the statistical learning-based fault diagnosis methods are
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發(fā)表于 2025-3-22 01:36:14 | 只看該作者
Hybrid Intelligent Fault Diagnosis,) combination method is introduced, where the same input feature set is considered. Next, a multiple adaptive neuro-fuzzy inference systems combination approaches with different input feature sets is demonstrated and validated using bearing fault diagnosis cases. Afterwards, a multidimensional hybri
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https://doi.org/10.1007/978-981-16-9131-7Intelligent fault diagnosis; Remaining useful life; Rotating machinery; Industrial big data; Deep learni
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