Description
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
Table of Contents
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
