Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis /
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 synthesi...
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| フォーマット: | 電子媒体 eBook |
| 言語: | 英語 |
| 出版事項: |
Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA :
Elsevier,
[2024]
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| 主題: | |
| オンライン･アクセス: | ScienceDirect |
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| 001 | on1409396826 | ||
| 003 | OCoLC | ||
| 005 | 20260915095226.0 | ||
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| 007 | cr cnu---unuuu | ||
| 008 | 231116s2024 ne ob 001 0 eng d | ||
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| 024 | 7 | |a 10.1016/C2021-0-00181-4 |2 doi | |
| 035 | |a (OCoLC)1409396826 |z (OCoLC)1409349712 |z (OCoLC)1535408721 |z (OCoLC)1561181595 | ||
| 050 | 4 | |a Q325.787 |b .Y35 2024 | |
| 082 | 0 | 4 | |a 006.31 |2 23/eng/20231222 |1 https://id.oclc.org/worldcat/ddc/E44XrjQ697wjjXyvKQFWbgh9gD |
| 100 | 1 | |a Yan, Ruqiang. | |
| 245 | 1 | 0 | |a Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis / |c Ruqiang Yan, Fei Shen. |
| 264 | 1 | |a Amsterdam, Netherlands ; |a Oxford, United Kingdom ; |a Cambridge MA : |b Elsevier, |c [2024] | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Front Cover -- Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis -- Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis -- Copyright -- Contents -- Author biography -- Preface -- One -- Introduction of machine fault diagnosis and prognosis -- 1.1 Background of machine fault diagnosis and prognosis -- 1.2 Machine fault diagnosis and prognosis technology with artificial intelligence -- 1.2.1 Fault diagnosis of rotating machinery based on new generation AI technology -- 1.2.2 RUL prediction of rotating machinery based on new generation AI technology | |
| 505 | 8 | |a 1.3 Machine fault diagnosis and prognosis technology with transfer learning -- 1.3.1 Research on rotating machinery fault diagnosis based on transfer learning -- 1.3.2 Research on rotating machinery RUL prediction based on transfer learning -- 1.4 Current problems and potential solutions -- References -- Two -- Foundations on transfer learning in machine fault diagnosis and prognosis -- 2.1 From machine learning to transfer learning -- 2.2 Model structure of transfer learning -- 2.2.1 Parameter-based knowledge transfer -- 2.2.2 Instance-based knowledge transfer | |
| 505 | 8 | |a 2.2.3 Feature-based knowledge transfer -- 2.2.4 Relevance-based knowledge transfer -- 2.3 The necessity of transfer learning -- 2.4 Negative transfer -- 2.5 Transfer components of machine fault diagnosis and prognosis models -- 2.6 Transfer fields of machine fault diagnosis and prognosis models -- 2.6.1 Transfer tasks across channels -- 2.6.2 Transfer between multiple machines -- 2.7 Transfer orders of machine fault diagnosis and prognosis models -- References -- Three -- Fault diagnosis models based on feature/sample transfer components | |
| 505 | 8 | |a 3.1 Machine fault diagnosis based on improved least squares support vector machines -- 3.1.1 Least squares support vector machine -- 3.1.2 Multitask LSSVM -- 3.1.3 The NMPT framework for GFD -- 3.1.4 Complete process of the NMPT model for gear fault diagnosis -- 3.1.5 Experiment and discussion -- 3.2 Machine fault diagnosis model based on hybrid transfer strategy -- 3.2.1 Overall framework of hybrid transfer strategy -- 3.2.2 Multidomain feature extraction -- 3.2.3 Signed rank and chi-square test-based similarity estimation -- 3.2.4 Hybrid transfer-based gear fault diagnosis | |
| 505 | 8 | |a 3.2.4.1 Low-quality source domains: The fast TrAdaBoost algorithm -- 3.2.4.2 High-quality source domains: The PMT algorithm -- 3.2.5 Experimentation and performance analysis -- References -- Four -- Fault diagnosis models based on cross time field transfer -- 4.1 Introduction -- 4.2 Machine fault diagnosis model based on dimensionality reduction projection -- 4.2.1 Basic assumptions -- 4.2.2 Dimensionality reduction projection -- 4.2.3 Building projection model -- 4.3 Machine fault diagnosis model based on locally weighted enhanced maximum interval projection | |
| 588 | |a Description based on online resource; title from digital title page (viewed on December 22, 2023). | ||
| 520 | |a 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. | ||
| 650 | 0 | |a Transfer learning (Machine learning) | |
| 650 | 0 | |a Fault location (Engineering) |x Data processing. | |
| 650 | 0 | |a Machinery. | |
| 650 | 6 | |a Apprentissage par transfert (Intelligence artificielle) |0 (CaQQLa)000347442 | |
| 650 | 6 | |a Détection de défaut (Ingénierie) |0 (CaQQLa)201-0138143 |x Informatique. |0 (CaQQLa)201-0380011 | |
| 650 | 6 | |a Machines. |0 (CaQQLa)201-0063959 | |
| 650 | 7 | |a machinery. |2 aat |0 (CStmoGRI)aat300024839 | |
| 650 | 7 | |a Fault location (Engineering) |x Data processing |2 fast |0 (OCoLC)fst00921984 | |
| 650 | 7 | |a Machinery |2 fast |0 (OCoLC)fst01004959 | |
| 650 | 7 | |a Transfer learning (Machine learning) |2 fast |0 (OCoLC)fst02025596 | |
| 776 | 0 | 8 | |i Print version: |a Yan, Ruqiang |t Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis |d San Diego : Elsevier,c2023 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780323999892 |