旋转机械智能故障诊断与剩余寿命预测（英文版） pdf 夸克云 tct umd 下载 2025 azw3 kindle

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 investigati*** and real applicati*** 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 contributi*** 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 ***d by'real applicati***， and industrial experience is summarized based on the cases， effectively combining theory and practice.

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 Conclusi***

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 Conclusi***

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 Conclusi***

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 Conclusi***

References

Chapter 6 Remaining Useful Life Prediction

6．1 Background

6．2 Data-driven Prediction Methods

6．3 Model-Based Prediction Methods

6．4 Conclusi***

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 investigati*** and real applicati*** 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 contributi*** 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 ***d by'real applicati***， and industrial experience is summarized based on the cases， effectively combining theory and practice.