旋转机械智能故障诊断与剩余寿命预测（英文版）

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.

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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