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.
1. Chapter One : Introduction of machine fault diagnosis and prognosis - p. Page viii 2. Chapter Two : Foundations on transfer learning in machine fault diagnosis and prognosis - p. Page 10 3. Chapter Three : Fault diagnosis models based on feature/sample transfer components - p. Page 36 4. Chapter Four : Fault diagnosis models based on cross time field transfer - p. Page 86 5. Chapter Five : Fault diagnosis models based on cross channel field transfer - p. Page 140 6. Chapter Six : Fault diagnosis models based on cross machine field transfer - p. Page 170 7. Chapter Seven : Prognosis models driven by transfer orders - p. Page 208 8. Chapter Eight : Fault diagnosis and prognosis driven by deep transfer learning - p. Page 259 9. Chapter Nine : Summary - p. Page 290 10. Index - p. Page 290
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.
1. Chapter One : Introduction of machine fault diagnosis and prognosis - p. Page viii 2. Chapter Two : Foundations on transfer learning in machine fault diagnosis and prognosis - p. Page 10 3. Chapter Three : Fault diagnosis models based on feature/sample transfer components - p. Page 36 4. Chapter Four : Fault diagnosis models based on cross time field transfer - p. Page 86 5. Chapter Five : Fault diagnosis models based on cross channel field transfer - p. Page 140 6. Chapter Six : Fault diagnosis models based on cross machine field transfer - p. Page 170 7. Chapter Seven : Prognosis models driven by transfer orders - p. Page 208 8. Chapter Eight : Fault diagnosis and prognosis driven by deep transfer learning - p. Page 259 9. Chapter Nine : Summary - p. Page 290 10. Index - p. Page 290
