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Total Size:
12.7 MB
Info Hash:
B4406B8A7401A571ACCFA13DA108904BFD86FB9C
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Added:
June 21, 2025, 9 p.m.
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(Last updated: June 21, 2025, 9:01 p.m.)
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| ['Li Y. Big Data-Driven Intelligent Fault Diagnosis and Prognosis..Mechanical 2022.pdf'] | 0 bytes |
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| Uploaded by indexFroggy | Size 14.9 MB | Health [ 28 /8 ] | Added 2023-07-01 |
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| Uploaded by andryold1 | Size 41.6 MB | Health [ 34 /25 ] | Added 2025-06-03 |
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| Uploaded by andryold1 | Size 12.7 MB | Health [ 48 /23 ] | Added 2025-06-21 |
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| Uploaded by andryold1 | Size 36.1 MB | Health [ 40 /17 ] | Added 2025-08-09 |
NOTE
SOURCE: Li Y. Big Data-Driven Intelligent Fault Diagnosis and Prognosis..Mechanical 2022
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COVER
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MEDIAINFO
Textbook in PDF format 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
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