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。《Prognostics and Health Management for Intelligent Electromechanical Systems（智能机电系统PHM）》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。《Prognostics and Health Management for Intelligent Electromechanical Systems（智能机电系统PHM）》分为三篇12章，**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。
目錄：
Contents1 Introduction 11.1 Overview of Intelligent Electromechanical System 21.1.1 High-Speed Trains 21.1.2 Robots 41.1.3 New Energy Vehicles 51.2 Research Status of Prognostics and Health Management in Intelligent Electromechanical System 61.2.1 Fault Diagnosis 71.2.2 Remaining Useful Life Prediction 81.3 Methodology of Prognostics and Health Management in Intelligent Electromechanical System 101.3.1 Feature Extraction Method 101.3.2 Prediction Model 111.3.3 Error Modification Model 131.4 The Necessity of Big Data Embedding in Prognostics and Health Management for Intelligent Electromechanical Systems 141.5 Scope of the Book 16References 182 Feature Extraction of Bearing Vibration Signal 252.1 Introduction 252.2 Data Acquisition 262.3 Frequency Domain Feature Extraction 282.3.1 The Theoretical Basis of Continuous Wavelet Transform 282.3.2 Feature Extraction 312.3.3 Feature Evaluation 332.4 Decomposition-Based Feature Extraction 352.4.1 The Theoretical Basis of Variational Modal Decomposition 352.4.2 Feature Extraction 362.4.3 Feature Evaluation 382.5 Deep Learning Feature Extraction 402.5.1 The Theoretical Basis of Convolutional Neural Network 402.5.2 Feature Extraction 412.5.3 Feature Evaluation 43References 453 Ensemble Intelligent Diagnosis for Bearing Faults 493.1 Introduction 493.2 Data Acquisition 503.3 Ensemble Diagnostic Model Based on Multi-objective Grey Wolf Optimizer for Bearing Faults 503.3.1 The Theoretical Basis of Empirical Wavelet Transform 503.3.2 The Theoretical Basis of Random Tree 533.3.3 The Theoretical Basis of Multi-objective Grey Wolf Optimizer 543.3.4 Experimental Result and Analysis 553.4 Boosting Ensemble Diagnostic Model for Bearing Faults 603.4.1 The Theoretical Basis of Empirical Mode Decomposition 603.4.2 The Theoretical Basis of Boosting 603.4.3 The Theoretical Basis of the Osprey-Cauchy-Sparrow Search Algorithm 633.4.4 Experimental Result and Analysis 653.5 Model Performance Comparison 693.6 Conclusions 70References 714 Deep Learning Prediction for Bearing Remaining Useful Life 734.1 Introduction 734.2 Data Acquisition 744.3 BiLSTM-Based Predictive Model for Bearing Remaining Useful Life 774.3.1 The Theoretical Basis Convolutional Neural Network 774.3.2 The Theoretical Basis Bidirectional Long Short-Term Memory 794.3.3 Experimental Result and Analysis 804.4 GRU-Based Predictive Model for Bearing Remaining Useful Life 824.4.1 The Theoretical Basis Gate Recurrent Unit 824.4.2 The Theoretical Basis Attention 83Contents v4.4.3 Experimental Result and Analysis 844.5 Model Performance Comparison 864.6 Conclusions 87References 895 Optimization Based Prediction for IGBT Remaining Useful Life 915.1 Introduction 915.2 Data Acquisition 925.3 Predictive Model for IGBT Remaining Useful Life Based on Particle Swarm Optimization 925.3.1 Health Indicator Based on Particle Swarm Optimization 925.3.2 RUL Prediction Based on the Similarity 955.4 Predictive Model for IGBT Remaining Useful Life Based on Bat Optimization 965.5 Model Performance Comparison 975.6 Application in Front-Wheel Steering Mobile Robot Fault-Tolerant Control 995.6.1 Front-Wheel Steering Mobile Robot System 995.6.2 Control Design 1015.6.3 Simulation Results 1035.7 Conclusions 109References 1106 Decomposition Based Prediction for MOSFET Remaining Useful Life 1136.1 Introduction 1136.2 Data Acquisition 1146.3 Predictive Model for MOSFET Remaining Useful Life Based on Wavelet Packet Decomposition 1146.3.1 Feature Extraction Based on Wavelet Packet Decomposition 1146.3.2 The Theoretical Basis of Autoregressive Integrated Moving Average Model 1166.3.3 Experimental Result and Analysis 1196.4 Predictive Model for MOSFET Remaining Useful Life Based on Complete Ensemble Empirical Mode Decomposition 1206.4.1 Feature Extraction Based on Complete Ensemble Empirical Mode Decomposition 1206.4.2 The Theoretical Basis of Long Short-Term Memory Model 1216.4.3 Experimental Result and Analysis 1236.5 Model Performance Comparison 1246.6 Applications in Wheeled Mobile Robot Fault-Tolerant Control 1266.6.1 Fault-Tolerant Control 1266.6.2 Applications in Wheeled Mobile Robot 1296.6.3 Performance Analysis 1316.7 Conclusions 134References 1347 Linear Networks and Temporal Convolution Based Prediction for Capacitor Remaining Useful Life 1377.1 Introduction 1377.2 Data Acquisition 1387.3 Predictive Model for Capacitor Remaining Useful Life Based on MSD-Mixer 1397.3.1 The Theoretical Basis Linear Network 1397.3.2 The Theoretical Basis of MSD-Mixer 1427.3.3 Experimental Result and Analysis 1447.4 Predictive Model for Capacitor Remaining Useful Life
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