Transfer Learning for Rotary Machine Fault Diagnosis and Prognosis / Ruqiang Yan, Fei Shen.

Format:

Book

Author/Creator:

Yan, Ruqiang.

Contributor:

ProQuest ebook central

Language:

English

Subjects (All):

Transfer learning (Machine learning).

Fault location (Engineering)--Data processing.

Fault location (Engineering).

Machinery.

Physical Description:

1 online resource

Place of Publication:

Amsterdam, Netherlands ; Oxford, United Kingdom ; Cambridge MA : Elsevier, [2024]

Contents:

Front Cover

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

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

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

Three

Fault diagnosis models based on feature/sample transfer components

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

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

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

Notes:

Includes bibliographical references and index.

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on online resource; title from digital title page (viewed on December 22, 2023).

ISBN:

0323914233

9780323914239

Publisher Number:

40032083137

Access Restriction:

Restricted for use by site license.