- 智能机电系统PHM(英文版)(精)
- - 作者：刘辉//成芳//李燕飞
  - 出版社：科学
  - ISBN：9787030825827
  - 出版日期：2025/01/01
  - 页数：208
- 售价：67.2

内容大纲
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。
作者介绍
目录
1  Introduction
  1.1  Overview of Intelligent Electromechanical System
    1.1.1  High-Speed Trains
    1.1.2  Robots
    1.1.3  New Energy Vehicles
  1.2  Research Status of Prognostics and Health Management in Intelligent Electromechanical System
    1.2.1  Fault Diagnosis
    1.2.2  Remaining Useful Life Prediction
  1.3  Methodology of Prognostics and Health Management in Intelligent Electromechanical System
    1.3.1  Feature Extraction Method
    1.3.2  Prediction Model
    1.3.3  Error Modification Model
  1.4  The Necessity of Big Data Embedding in Prognostics and Health Management for Intelligent Electromechanical Systems
  1.5  Scope of the Book
  References
2  Feature Extraction of Bearing Vibration Signal
  2.1  Introduction
  2.2  Data Acquisition
  2.3  Frequency Domain Feature Extraction
    2.3.1  The Theoretical Basis of Continuous Wavelet Transform
    2.3.2  Feature Extraction
    2.3.3  Feature Evaluation
  2.4  Decomposition-Based Feature Extraction
    2.4.1  The Theoretical Basis of Variational Modal Decomposition
    2.4.2  Feature Extraction
    2.4.3  Feature Evaluation
  2.5  Deep Learning Feature Extraction
    2.5.1  The Theoretical Basis of Convolutional Neural Network
    2.5.2  Feature Extraction
    2.5.3  Feature Evaluation
  References
3  Ensemble Intelligent Diagnosis for Bearing Faults
  3.1  Introduction
  3.2  Data Acquisition
  3.3  Ensemble Diagnostic Model Based on Multi-objective Grey Wolf Optimizer for Bearing Faults
    3.3.1  The Theoretical Basis of Empirical Wavelet Transform
    3.3.2  The Theoretical Basis of Random Tree
    3.3.3  The Theoretical Basis of Multi-objective Grey Wolf Optimizer
    3.3.4  Experimental Result and Analysis
  3.4  Boosting Ensemble Diagnostic Model for Bearing Faults
    3.4.1  The Theoretical Basis of Empirical Mode Decomposition
    3.4.2  The Theoretical Basis of Boosting
    3.4.3  The Theoretical Basis of the Osprey-Cauchy-Sparrow Search Algorithm
    3.4.4  Experimental Result and Analysis
  3.5  Model Performance Comparison
  3.6  Conclusions
  References
4  Deep Learning Prediction for Bearing Remaining Useful Life
  4.1  Introduction
  4.2  Data Acquisition

  4.3  BiLSTM-Based Predictive Model for Bearing Remaining Useful Life
    4.3.1  The Theoretical Basis Convolutional Neural Network
    4.3.2  The Theoretical Basis Bidirectional Long Short-Term Memory
    4.3.3  Experimental Result and Analysis
  4.4  GRU-Based Predictive Model for Bearing Remaining Useful Life
    4.4.1  The Theoretical Basis Gate Recurrent Unit
    4.4.2  The Theoretical Basis Attention
    4.4.3  Experimental Result and Analysis
  4.5  Model Performance Comparison
  4.6  Conclusions
  References
5  Optimization Based Prediction for IGBT Remaining Useful Life
  5.1  Introduction
  5.2  Data Acquisition
  5.3  Predictive Model for IGBT Remaining Useful Life Based on Particle Swarm Optimization
    5.3.1  Health Indicator Based on Particle Swarm Optimization
    5.3.2  RUL Prediction Based on the Similarity
  5.4  Predictive Model for IGBT Remaining Useful Life Based on Bat Optimization
  5.5  Model Performance Comparison
  5.6  Application in Front-Wheel Steering Mobile Robot Fault-Tolerant Control
    5.6.1  Front-Wheel Steering Mobile Robot System
    5.6.2  Control Design
    5.6.3  Simulation Results
  5.7  Conclusions
  References
6  Decomposition Based Prediction for MOSFET Remaining Useful Life
  6.1  Introduction
  6.2  Data Acquisition
  6.3  Predictive Model for MOSFET Remaining Useful Life Based on Wavelet Packet Decomposition
    6.3.1  Feature Extraction Based on Wavelet Packet Decomposition
    6.3.2  The Theoretical Basis of Autoregressive Integrated Moving Average Model
    6.3.3  Experimental Result and Analysis
  6.4  Predictive Model for MOSFET Remaining Useful Life Based on Complete Ensemble Empirical Mode Decomposition
    6.4.1  Feature Extraction Based on Complete Ensemble Empirical Mode Decomposition
    6.4.2  The Theoretical Basis of Long Short-Term Memory Model
    6.4.3  Experimental Result and Analysis
  6.5  Model Performance Comparison
  6.6  Applications in Wheeled Mobile Robot Fault-Tolerant Control
    6.6.1  Fault-Tolerant Control
    6.6.2  Applications in Wheeled Mobile Robot
    6.6.3  Performance Analysis
  6.7  Conclusions
  References
7  Linear Networks and Temporal Convolution Based Prediction for Capacitor Remaining Useful Life
  7.1  Introduction
  7.2  Data Acquisition
  7.3  Predictive Model for Capacitor Remaining Useful Life Based on MSD-Mixer
    7.3.1  The Theoretical Basis Linear Network
    7.3.2  The Theoretical Basis of MSD-Mixer
    7.3.3  Experimental Result and Analysis

  7.4  Predictive Model for Capacitor Remaining Useful Life Based on TimesNet
    7.4.1  The Theoretical Basis of Temporal Convolutional Networks
    7.4.2  The Theoretical Basis of TimesNet
    7.4.3  Experimental Result and Analysis
  7.5  Model Performance Comparison
  7.6  Conclusions
  References
8  Remaining Useful Life Prediction of Power Supply Based on Range-Extended New Energy Vehicles
  8.1  Introduction
  8.2  Data Acquisition
  8.3  Predictive Model for Power Supply Remaining Useful Life Based on FEDformer
    8.3.1  The Theoretical Basis of Transformer
    8.3.2  The Theoretical Basis of FEDformer
    8.3.3  Experimental Result and Analysis
  8.4  Predictive Model for Power Supply Remaining Useful Life Based on Preformer
    8.4.1  The Theoretical Basis of Multi-scale Time–Frequency Analysis of Power Batteries
    8.4.2  The Theoretical Basis of Preformer
    8.4.3  Experimental Result and Analysis
  8.5  Model Performance Comparison
  8.6  Conclusions
  References
9  Big Data Embedding in PHM for Electromechanical System
  9.1  Introduction
  9.2  Construction of Big Data Storage Platform
    9.2.1  Data Source and Acquisition
    9.2.2  Data Storage and Management Technology
  9.3  Distributed Predictive Model for Electromechanical System
    9.3.1  Distributed Computing Framework
    9.3.2  Case Study
    9.3.3  Challenges and Analysis
  9.4  Conclusions
  References

内容大纲

作者介绍

目录

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