Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis
introduces the theory and latest applications of transfer learning
on rotary machine fault diagnosis and prognosis. Transfer
learning-based rotary machine fault diagnosis is a relatively new
subject, and this innovative book synthesizes recent advances from
academia and industry to provide systematic guidance. Basic
principles are described before key questions are answered,
including the applicability of transfer learning to rotary machine
fault diagnosis and prognosis, technical details of models, and an
introduction to deep transfer learning. Case studies for every
method are provided, helping readers apply the techniques described
in their own work.
General
| Imprint: |
Elsevier - Health Sciences Division
|
| Country of origin: |
United States |
| Release date: |
October 2023 |
| First published: |
2024 |
| Authors: |
Ruqiang Yan
• Fei Shen
|
| Dimensions: |
229 x 152mm (L x W) |
| Format: |
Paperback
|
| Pages: |
300 |
| ISBN-13: |
978-0-323-99989-2 |
| Categories: |
Books
|
| LSN: |
0-323-99989-1 |
| Barcode: |
9780323999892 |
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