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旋转机械智能故障诊断与剩余寿命预测（英文版）书籍详细信息
- ISBN：9787560594002
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2017-4
- 页数：暂无页数
- 价格：198.00元
- 纸张：暂无纸张
- 装帧：暂无装帧
- 开本：暂无开本
- 语言：未知
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内容简介：
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery outlines the fundamental theories and the advanced methods of intelligent fault diagnosis and the remaining useful life （RUL） prediction for rotating machinery. These methods are paralleled by experimental investigations and real applications for rotors，rolling element bearings， and gears. The book I~rovides a guide to the basic concepts，fundamental theories， and cutting-edge research， and is especially useful for readers engaged in prognostics and health management. Features Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery， involving fault mechanisms， vibration characteristics， health indicators， diagnostics and prognostics. Presentsbasic theories， advanced methods， and the latest contributions in the field of intelligent fault diagnosis and RUL prediction， such as deep learning based intelligent fault diagnosis， adaptive clustering algorithm based fault identification，multidimensional hybrid intelligent diagnosis， and data-driven or model-based RUL prediction methods. Presents numerous case studies. The methods， algorithms， and models introduced in the book are demonstrated by'real applications， and industrial experience is summarized based on the cases， effectively combining theory and practice.
书籍目录：
Preface
Chapter 1 Introduction and Background
1，1 Introduction
1．2 Overview of PHM
1．3 Preface to Book Chapters
References
Chapter 2 Signal Processing and Feature Extraction
2．1 Introduction
2．2 Signal Preprocessing
2．3 Signal Processing in theTime Domain
2．4 Signal Processing in the Frequency Domain
2．5 Signal Processing in theTime-Frequency Domain
2．6 Conclusions
References
Chapter 3 Individual Intelligent Method-Based Fault Diagnosis
3．1 Introduction to Intelligent Diagnosis Methods
3．2 Artificial Neural Networks
3．3 Statistical LearningTheory
3．4 Deep Learning
3．5 Conclusions
References
Chapter 4 Clustering Algorithm-Based Fault Diagnosis
4．1 Introduction to Clustering Algorithm
4．2 Weighted K Nearest Neighbor-Based Fault Diagnosis
4．3 Weighted Fuzzy c-Means-Based Fault Diagnosis
4．4 Hybrid Clustering Algorithm-Based Fault Diagnosis
4．5 Conclusions
References
Chapter 5 Hybrid Intelligent Fault Diagnosis Methods
5．1 Introduction
5．2 MultipleWKNN Combination-Based Fault Diagnosis
5．3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis
5．4 A Multidimensional Hybrid Intelligent Method
5．5 Conclusions
References
Chapter 6 Remaining Useful Life Prediction
6．1 Background
6．2 Data-driven Prediction Methods
6．3 Model-Based Prediction Methods
6．4 Conclusions
References
Glossary
Index
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书籍介绍
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery outlines the fundamental theories and the advanced methods of intelligent fault diagnosis and the remaining useful life （RUL） prediction for rotating machinery. These methods are paralleled by experimental investigations and real applications for rotors，rolling element bearings， and gears. The book I~rovides a guide to the basic concepts，fundamental theories， and cutting-edge research， and is especially useful for readers engaged in prognostics and health management. Features Provides a detailed background and roadmap of intelligent diagnosis and RUL prediction of rotating machinery， involving fault mechanisms， vibration characteristics， health indicators， diagnostics and prognostics. Presentsbasic theories， advanced methods， and the latest contributions in the field of intelligent fault diagnosis and RUL prediction， such as deep learning based intelligent fault diagnosis， adaptive clustering algorithm based fault identification，multidimensional hybrid intelligent diagnosis， and data-driven or model-based RUL prediction methods. Presents numerous case studies. The methods， algorithms， and models introduced in the book are demonstrated by'real applications， and industrial experience is summarized based on the cases， effectively combining theory and practice.
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