機電系統(tǒng)是大部分電氣機械設(shè)備的基本功能基礎(chǔ)，機電系統(tǒng)的故障診斷與健康管理（PHM）對整個機械設(shè)備的安全運行具有至關(guān)重要的意義。本書結(jié)合大數(shù)據(jù)技術(shù)在機電系統(tǒng)PHM中的應(yīng)用，全面介紹了智能機電系統(tǒng)PHM的相關(guān)理論、關(guān)鍵技術(shù)和應(yīng)用實例。全書分為三篇12章，第一篇從機電系統(tǒng)PHM重要性進行分析，介紹了智能機電系統(tǒng)及其研究現(xiàn)狀和方法，并介紹智能機電系統(tǒng)PHM嵌入大數(shù)據(jù)的必要性；第二篇以軸承為例介紹機械系統(tǒng)的PHM大數(shù)據(jù)方法，包括：第2章介紹軸承振動信號的特征提取方法，第3章介紹軸承剩余壽命的集成智能預(yù)測方法，第4章介紹軸承故障集成智能診斷方法，第5章介紹軸承剩余壽命的深度預(yù)測方法，第6章介紹軸承故障深度診斷方法，第7章介紹將機械系統(tǒng)PHM大數(shù)據(jù)嵌入方法；第三篇介紹電氣系統(tǒng)的PHM大數(shù)據(jù)方法，包括：第8章介紹IGBT的剩余壽命優(yōu)化預(yù)測方法，第9章介紹MOSFET剩余壽命分解預(yù)測方法，第10章介紹電容剩余壽命的誤差修正預(yù)測方法，第11章介紹電源剩余壽命的濾波修正預(yù)測方法，第12章以電源為例介紹電氣系統(tǒng)PHM大數(shù)據(jù)嵌入方法。 各章內(nèi)容都具有實例分析，幫助讀者深入理解相關(guān)內(nèi)容，激發(fā)靈感。
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劉輝帶領(lǐng)國際化的研究團隊在"自動化"和"智能交通"領(lǐng)域所取得的研究成果引起國際極大關(guān)注，曾被德國主流新聞媒體"波羅的海日報"Ostsee-Zeitung以"人物專訪"的形式進行整版報道，并被德國最具影響力的技術(shù)評論期刊之一"Laborjournal"評為全德國2014年度"實驗室自動化"領(lǐng)域最優(yōu)秀的四個技術(shù)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
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