Intro
Preface
Contents
About the Authors
1 Introduction and Background
1.1 Introduction
1.1.1 AI Technologies for Data Processing
1.1.2 Big Data-Driven Intelligent Predictive Maintenance
1.1.3 Big Data Analytics Platform Practices
1.2 Overview of Big Data-Driven PHM
1.2.1 Data Acquisition
1.2.2 Data Processing
1.2.3 Diagnosis
1.2.4 Prognosis
1.2.5 Maintenance
1.3 Preface to Book Chapters
References
2 Conventional Intelligent Fault Diagnosis
2.1 Introduction
2.2 Typical Neural Network-Based Methods

2.2.1 Introduction to Neural Networks
2.2.2 Intelligent Diagnosis Using Radial Basis Function Network
2.2.3 Intelligent Diagnosis Using Wavelet Neural Network
2.2.4 Epilog
2.3 Statistical Learning-Based Methods
2.3.1 Introduction to Statistical Learning
2.3.2 Intelligent Diagnosis Using Support Vector Machine
2.3.3 Intelligent Diagnosis Using Relevant Vector Machine
2.3.4 Epilog
2.4 Conclusions
References
3 Hybrid Intelligent Fault Diagnosis
3.1 Introduction
3.2 Multiple WKNN Fault Diagnosis
3.2.1 Motivation

3.2.2 Diagnosis Model Based on Combination of Multiple WKNN
3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings
3.2.4 Epilog
3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis
3.3.1 Motivation
3.3.2 Multiple ANFIS Combination with GA
3.3.3 Fault Diagnosis Method Based on Multiple ANFIS Combination
3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings
3.3.5 Epilog
3.4 A Multidimensional Hybrid Intelligent Method
3.4.1 Motivation
3.4.2 Multiple Classifier Combination
3.4.3 Diagnosis Method Based on Multiple Classifier Combination

3.4.4 Intelligent Diagnosis Case of Gearboxes
3.4.5 Epilog
3.5 Conclusions
References
4 Deep Transfer Learning-Based Intelligent Fault Diagnosis
4.1 Introduction
4.2 Deep Belief Network for Few-Shot Fault Diagnosis
4.2.1 Motivation
4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning
4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots
4.2.4 Epilog
4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data
4.3.1 Motivation
4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model

4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data
4.3.4 Epilog
4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis
4.4.1 Motivation
4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model
4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry
4.4.4 Epilog
4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis
4.5.1 Motivation
4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model
4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets
4.5.4 Epilog