Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis

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. Offers case studies for each transfer learning algorithm Optimizes the transfer learning models to solve specific engineering problems Describes the roles of transfer components, transfer fields, and transfer order in intelligent machine diagnosis and prognosis INDICE: 1. Introduction of machine fault diagnosis and prognosis2. The basic principle of transfer learning-based mechanical fault diagnosis and prognosis3. Fault diagnosis models based on sample transfer components4. Fault diagnosis models based on feature transfer components5. Fault diagnosis models based on feature transfer components6. Fault diagnosis models based on feature transfer components7. Fault diagnosis models based on feature transfer components8. Prognosis models driven by transfer orders9. Fault Diagnosis and Prognosis driven by deep transfer learning10. Summary

• ISBN: 978-0-323-99989-2
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 300
• Fecha Publicación: 01/06/2023
• Nº Volúmenes: 1
• Idioma: Inglés