Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis

Ruqiang Yan, Fei Shen

• Publisher: Elsevier
• Year: 2023
• Print ISBN: 9780323999892
• eBook ISBN: 9780323914239
• Topic: Business
• Subtopic: Insurance
• Index: Business

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

• Cover
• Front Matter
• Table of Contents
• Copyright
• Author biography
• Preface
• List of Illustrations
• List of Tables
• Chapter One : Introduction of machine fault diagnosis and prognosis
• Chapter Two : Foundations on transfer learning in machine fault diagnosis and prognosis
• Chapter Three : Fault diagnosis models based on feature/sample transfer components
• Chapter Four : Fault diagnosis models based on cross time field transfer
• Chapter Five : Fault diagnosis models based on cross channel field transfer
• Chapter Six : Fault diagnosis models based on cross machine field transfer
• Chapter Seven : Prognosis models driven by transfer orders
• Chapter Eight : Fault diagnosis and prognosis driven by deep transfer learning
• Chapter Nine : Summary
• Index
• A