Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis provides an introduction to 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 draws together recent advances from academia and industry to provide systematic guidance. The basic principles are described before key questions are answered including the applicability of transfer learning to rotary machine fault diagnosis and prognosis, the technical details of the models, and an intro to deep transfer learning. Case studies for every method are provided, helping readers to apply the techniques described in their own work. Comparisons with traditional machine learning methods are also discussed to facilitate the identification of where transfer learning should and should not be applied.