Inhoudsopgave:
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\u003ci\u003eTransfer Learning for Rotary Machine Fault Diagnosis and Prognosis\u003c/i\u003e 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.\u003cul\u003e \u003cli\u003eOffers case studies for each transfer learning algorithm\u003c/li\u003e \u003cli\u003eOptimizes the transfer learning models to solve specific engineering problems\u003c/li\u003e \u003cli\u003eDescribes the roles of transfer components, transfer fields, and transfer order in intelligent machine diagnosis and prognosis\u003c/li\u003e\u003c/ul\u003e |
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