机电系统是大部分电气机械设备的基本功能基础，机电系统的故障诊断与健康管理（PHM）对整个机械设备的安全运行具有至关重要的意义。本书结合大数据技术在机电系统PHM中的应用，全面介绍了智能机电系统PHM的相关理论、关键技术和应用实例。全书分为三篇12章，第一篇从机电系统PHM重要性进行分析，介绍了智能机电系统及其研究现状和方法，并介绍智能机电系统PHM嵌入大数据的必要性；第二篇以轴承为例介绍机械系统的PHM大数据方法，包括：第2章介绍轴承振动信号的特征提取方法，第3章介绍轴承剩余寿命的集成智能预测方法，第4章介绍轴承故障集成智能诊断方法，第5章介绍轴承剩余寿命的深度预测方法，第6章介绍轴承故障深度诊断方法，第7章介绍将机械系统PHM大数据嵌入方法；第三篇介绍电气系统的PHM大数据方法，包括：第8章介绍IGBT的剩余寿命优化预测方法，第9章介绍MOSFET剩余寿命分解预测方法，第10章介绍电容剩余寿命的误差修正预测方法，第11章介绍电源剩余寿命的滤波修正预测方法，第12章以电源为例介绍电气系统PHM大数据嵌入方法。 各章内容都具有实例分析，帮助读者深入理解相关内容，激发灵感。
更多科学出版社服务，请扫码获取。
刘辉带领国际化的研究团队在"自动化"和"智能交通"领域所取得的研究成果引起国际极大关注，曾被德国主流新闻媒体"波罗的海日报"Ostsee-Zeitung以"人物专访"的形式进行整版报道，并被德国最具影响力的技术评论期刊之一"Laborjournal"评为全德国2014年度"实验室自动化"领域最优秀的四个技术成果之一。
Contents
1 Introduction 1
1.1 Overview of Intelligent Electromechanical System 2
1.1.1 High-Speed Trains 2
1.1.2 Robots 4
1.1.3 New Energy Vehicles 5
1.2 Research Status of Prognostics and Health Management in Intelligent Electromechanical System 6
1.2.1 Fault Diagnosis 7
1.2.2 Remaining Useful Life Prediction 8
1.3 Methodology of Prognostics and Health Management in Intelligent Electromechanical System 10
1.3.1 Feature Extraction Method 10
1.3.2 Prediction Model 11
1.3.3 Error Modification Model 13
1.4 The Necessity of Big Data Embedding in Prognostics and Health Management for Intelligent Electromechanical Systems 14
1.5 Scope of the Book 16
References 18
2 Feature Extraction of Bearing Vibration Signal 25
2.1 Introduction 25
2.2 Data Acquisition 26
2.3 Frequency Domain Feature Extraction 28
2.3.1 The Theoretical Basis of Continuous Wavelet Transform 28
2.3.2 Feature Extraction 31
2.3.3 Feature Evaluation 33
2.4 Decomposition-Based Feature Extraction 35
2.4.1 The Theoretical Basis of Variational Modal Decomposition 35
2.4.2 Feature Extraction 36
2.4.3 Feature Evaluation 38
2.5 Deep Learning Feature Extraction 40
2.5.1 The Theoretical Basis of Convolutional Neural Network 40
2.5.2 Feature Extraction 41
2.5.3 Feature Evaluation 43
References 45
3 Ensemble Intelligent Diagnosis for Bearing Faults 49
3.1 Introduction 49
3.2 Data Acquisition 50
3.3 Ensemble Diagnostic Model Based on Multi-objective Grey Wolf Optimizer for Bearing Faults 50
3.3.1 The Theoretical Basis of Empirical Wavelet Transform 50
3.3.2 The Theoretical Basis of Random Tree 53
3.3.3 The Theoretical Basis of Multi-objective Grey Wolf Optimizer 54
3.3.4 Experimental Result and Analysis 55
3.4 Boosting Ensemble Diagnostic Model for Bearing Faults 60
3.4.1 The Theoretical Basis of Empirical Mode Decomposition 60
3.4.2 The Theoretical Basis of Boosting 60
3.4.3 The Theoretical Basis of the Osprey-Cauchy-Sparrow Search Algorithm 63
3.4.4 Experimental Result and Analysis 65
3.5 Model Performance Comparison 69
3.6 Conclusions 70
References 71
4 Deep Learning Prediction for Bearing Remaining Useful Life 73
4.1 Introduction 73
4.2 Data Acquisition 74
4.3 BiLSTM-Based Predictive Model for Bearing Remaining Useful Life 77
4.3.1 The Theoretical Basis Convolutional Neural Network 77
4.3.2 The Theoretical Basis Bidirectional Long Short-Term Memory 79
4.3.3 Experimental Result and Analysis 80
4.4 GRU-Based Predictive Model for Bearing Remaining Useful Life 82
4.4.1 The Theoretical Basis Gate Recurrent Unit 82
4.4.2 The Theoretical Basis Attention 83
Contents v
4.4.3 Experimental Result and Analysis 84
4.5 Model Performance Comparison 86
4.6 Conclusions 87
References 89
5 Optimization Based Prediction for IGBT Remaining Useful Life 91
5.1 Introduction 91
5.2 Data Acquisition 92
5.3 Predictive Model for IGBT Remaining Useful Life Based on Particle Swarm Optimization 92
5.3.1 Health Indicator Based on Particle Swarm Optimization 92
5.3.2 RUL Prediction Based on the Similarity 95
5.4 Predictive Model for IGBT Remaining Useful Life Based on Bat Optimization 96
5.5 Model Performance Comparison 97
5.6 Application in Front-Wheel Steering Mobile Robot Fault-Tolerant Control 99
5.6.1 Front-Wheel Steering Mobile Robot System 99
5.6.2 Control Design 101
5.6.3 Simulation Results 103
5.7 Conclusions 109
References 110
6 Decomposition Based Prediction for MOSFET Remaining Useful Life 113
6.1 Introduction 113
6.2 Data Acquisition 114
6.3 Predictive Model for MOSFET Remaining Useful Life Based on Wavelet Packet Decomposition 114
6.3.1 Feature Extraction Based on Wavelet Packet Decomposition 114
6.3.2 The Theoretical Basis of Autoregressive Integrated Moving Average Model 116
6.3.3 Experimental Result and Analysis 119
6.4 Predictive Model for MOSFET Remaining Useful Life Based on Complete Ensemble Empirical Mode Decomposition 120
6.4.1 Feature Extraction Based on Complete Ensemble Empirical Mode Decomposition 120
6.4.2 The Theoretical Basis of Long Short-Term Memory Model 121
6.4.3 Experimental Result and Analysis 123
6.5 Model Performance Comparison 124
6.6 Applications in Wheeled Mobile Robot Fault-Tolerant Control 126
6.6.1 Fault-Tolerant Control 126
6.6.2 Applications in Wheeled Mobile Robot 129
6.6.3 Performance Analysis 131
6.7 Conclusions 134
References 134
7 Linear Networks and Temporal Convolution Based Prediction for Capacitor Remaining Useful Life 137
7.1 Introduction 137
7.2 Data Acquisition 138
7.3 Predictive Model for Capacitor Remaining Useful Life Based on MSD-Mixer 139
7.3.1 The Theoretical Basis Linear Network 139
7.3.2 The Theoretical Basis of MSD-Mixer 142
7.3.3 Experimental Result and Analysis 144
7.4 Predictive Model for Capacitor Remaining Useful Life Based on TimesNet 145
7.4.1 The Theoretical Basis of Temporal Convolutional Networks 145
7.4.2 The Theoretical Basis of TimesNet 149
7.4.3 Experimental Result and Analysis 151
7.5 Model Performance Comparison 152
7.6 Conclusions 154
References 155
8 Remaining Useful Life Prediction of Power Supply Based on Range-Extended New Energy Vehicles 159
8.1 Introduction 159
8.2 Data Acquisition 160
8.3 Predictive Model for Power Supply Remaining Useful Life Based on FEDformer 165
8.3.1 The Theoretical Basis of Transformer 165
8.3.2 The Theoretical Basis of FEDformer 168
8.3.3 Experimental Result and Analysis 170
8.4 Predictive Model for Power Supply Remaining Useful Life Based on Preformer 173
8.4.1 The Theoretical Basis of Multi-scale Time–Frequency Analysis of Power Batteries 173
8.4.2 The Theoretical Basis of Preformer 175
8.4.3 Experimental Result and Analysis 177
8.5 Model Performance Comparison 179
8.6 Conclusions 181
References 182
9 Big Data Embedding in PHM for Electromechanical System 185
9.1 Introduction 185
9.2 Construction of Big Data Storage Platform 187
9.2.1 Data Source and Acquisition 187
9.2.2 Data Storage and Management Technology 189
9.3 Distributed Predictive Model for Electromechanical System 193
9.3.1 Distributed Computing Framework 195
9.3.2 Case Study 197
9.3.3 Challenges and Analysis 204
9.4 Conclusions 206
References 206
书单推荐
