Ruqiang Yan, Fei Shen

Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Health Sciences
• Seitenzahl: 312
• Erscheinungstermin: 15. November 2023
• Englisch
• Gewicht: 450g
• ISBN-13: 9780323999892
• ISBN-10: 0323999891
• Artikelnr.: 64140654

Autorenporträt

Ruqiang Yan is a Professor and phd supervisor at Xi¿an Jiaotong University, China. His main research interests include machine learning with emphasis on deep learning, transfer learning and their applications, data analytics, multi-domain signal processing, non-linear time-series analysis, structural health monitoring, and diagnosis and prognosis. He serves as the associate editor-in-chief in of IEEE Transactions on Instrumentation and Measurement. Dr. Yan has published over 10 Journal Papers related to transfer learning-based machine fault diagnosis and prognosis. He was the Principal Investigator of a project titled Transfer Learning Based Rotating Machine Fault Diagnosis and Remaining Useful Life Prediction, sponsored by the National Natural Science Foundation of China

Inhaltsangabe

1. Introduction to machine fault diagnosis and prognosis 2. The basic
principle of transfer learning-based mechanical fault diagnosis and
prognosis 3. Fault diagnosis models based on sample transfer components 4.
Fault diagnosis models based on feature transfer components 5. Fault
diagnosis models based on cross time fields transfer 6. Fault diagnosis
models based on cross channel fields transfer 7. Fault diagnosis models
based on cross machine fields transfer 8. Prognosis models driven by
transfer orders 9. Fault diagnosis and prognosis driven by deep transfer
learning 10. Summary