- 熵值理论及其在机械状态监测中的应用(英文版)
- - 作者：李永波//王先芝//邓子辰//司书宾//李玉庆
  - 出版社：科学
  - ISBN：9787030773777
  - 出版日期：2024/01/01
  - 页数：211
- 售价：75.2

内容大纲
本书系统地回顾了熵值理论发展，介绍了熵值方法的最新研究成果，详尽阐述了每种计算方法的定义、原理、性质、适用性及诊断机理，并给出每种方法在机械故障诊断中应用的典型案例。最后，讨论了熵值在未来的数据驱动故障诊断的应用前景和潜在研究趋势，为后续研究提供指引。主要内容包括：熵值理论的发展；熵值理论对比分析；基于熵值的智能故障诊断框架；散度熵；基于符号动力学滤波的熵值理论研究；多尺度熵的理论与应用；基于熵值理论的降噪方法研究；基于熵值理论的迁移诊断；熵值理论在变转速工况下的智能诊断方法；基于多元熵的大型旋转机械故障诊断方法；基于振荡排列熵的滚动轴承故障诊断方法。
作者介绍
目录
Preface
Chapter 1  Development of entropy theories
  1.1  From thermodynamic to information entropy
  1.2  Renyi entropy
  1.3  Kolmogorov-Sinai entropy
  1.4  Eckmann-Ruelle entropy
  1.5  Approximate entropy
  1.6  Sample entropy
  1.7  Fuzzy entropy
  1.8  Permutation entropy
  1.9  Conclusions
  1.10  References
Chapter 2  Comparative analysis of entropy methods on health condition monitoring of machines
  2.1  Comparisons of various entropy measures
  2.2  Quantitative comparison of entropy measures
  2.3  Effect of noise on entropy calculation
    2.3.1  Research on the effect of noise using a simulated model
    2.3.2  Performance comparison under strong noise
  2.4  Calculation efficiency
    2.4.1  Research on the calculation efficiency using simulation model
    2.4.2  Discussion on the calculation efficiency
  2.5  Effect of data length
  2.6  Classification performance
    2.6.1  Simulation model regarding classification performance
    2.6.2  Classification performances for different types of entropy algorithms
  2.7  Conclusions
  2.8  References
Chapter 3  Intelligent fault diagnosis based on entropy theories
  3.1  General procedure of the intelligent fault diagnosis
    3.1.1  Data collection
    3.1.2  Feature extraction
    3.1.3  Feature selection
    3.1.4  Pattern recognition
  3.2  Case study: intelligent fault diagnosis method based on modified multiscale symbolic dynamic entropy and mRMR
    3.2.1  MMSDE-mRMR-LSSVM method
    3.2.2  Experiment
  3.3  Conclusions
  3.4  References
Chapter 4  Diversity entropy
  4.1  Introduction: consistency problem of the entropy methods
  4.2  Methodology of diversity entropy
  4.3  Properties and simulation evaluation
    4.3.1  Consistency
    4.3.2  Robustness
    4.3.3  Calculation efficiency
  4.4  Case study: fault diagnosis of the dual-rotor system
    4.4.1  Fault diagnosis frame based on MDE and ELM
    4.4.2  Experiment setup
    4.4.3  Results and analysis
  4.5  Conclusions

  4.6  References
Chapter 5  Symbolic dynamic filtering based entropy methods
  5.1  Introduction
  5.2  Methods
    5.2.1  Symbolic dynamic filtering
    5.2.2  Symbolic dynamic entropy
    5.2.3  Symbolic fuzzy entropy
    5.2.4  Symbolic diversity entropy
  5.3  Numerical validation for symbolic fuzzy entropy
    5.3.1  Complexity measure
    5.3.2  Robustness to noise
    5.3.3  Computational complexity
  5.4  Case study: fault diagnosis of bearing system
    5.4.1  MSFE-based fault diagnosis method
    5.4.2  Test rig
    5.4.3  Results and analysis
  5.5  Conclusions
  5.6  References
Chapter 6  Multiscale based entropy methods
  6.1  Multiscale methods
    6.1.1  Multiscale entropy
    6.1.2  Composite multiscale entropy
    6.1.3  Modified multiscale entropy
    6.1.4  Refined composite multiscale entropy
  6.2  Generalized multiscale methods
    6.2.1  Generalized multiscale entropy
    6.2.2  Generalized composite multiscale entropy
    6.2.3  Generalized refined composite multiscale entropy
  6.3  Hierarchical multiscale methods
    6.3.1  Hierarchical entropy
    6.3.2  Modified hierarchical entropy
    6.3.3  Modified hierarchical generalized composite entropy
  6.4  Case study: multiscale entropy performance analysis
    6.4.1  Dataset
    6.4.2  Experiment setup
    6.4.3  Results and analysis
  6.5  Conclusions
  6.6  References
Chapter 7  Application of entropy methods in extracting weak fault characteristics by adaptive decomposition
  7.1  Introduction
    7.1.1  LMD
    7.1.2  The optimum PF component selection
    7.1.3  Improved mulfiscale fuzzy entropy
    7.1.4  Feature selection using Laplacian score algorithm
    7.1.5  Improved SVM-BT
  7.2  Fault diagnosis based on LMD and IMFE
  7.3  Case study: fault diagnosis of rolling bearing
    7.3.1  Experiment setup
    7.3.2  Results and analysis
  7.4  Conclusions

  7.5  References
Chapter 8  Intelligent fault diagnosis based on entropy theories and transfer learning
  8.1  Preliminary knowledge
    8.1.1  Concepts
    8.1.2  Single domain VS multisource domain
    8.1.3  The domain invariant properties of the entropy
  8.2  Transfer diagnosis from single source domain
    8.2.1  The application of entropy in single source domain transfer problems
    8.2.2  Multiscale transfer symbolic dynamic entropy method
    8.2.3  Case study
  8.3  Transfer diagnosis knowledge from multisource domain
    8.3.1  The application of entropy in multiple source domain transfer problems
    8.3.2  Multisource domain generalization based on dispersion entropy
    8.3.3  Case study
  8.4  Conclusions
  8.5  References
Chapter 9  Entropy-based fault diagnosis under variable rotational speed
  9.1  Introduction
  9.2  The bandwidth selection criterion for Vold-Kalman filter
  9.3  Fault diagnosis frame based on IVKF, MSE, LS and LSSVM
  9.4  Case study: fault diagnosis of planetary gearbox
    9.4.1  Experiment setup
    9.4.2  Results and analysis
  9.5  Conclusions
  9.6  References
Chapter 10  Multivariate entropy methods and fault diagnosis of large-scale machinery
  10.1  Introduction: multivariate entropy and large-scale machinery
  10.2  Multivariate entropy
    10.2.1  Multivariate multiscate sample entropy
    10.2.2  Multivariate multiscale fuzzy entropy
    10.2.3  Multivariate multiscale permutation entropy
  10.3  Variational embedding multiscale diversity entropy
  10.4  Simulation validation: the limitations of the multivariate entropy
    10.4.1  Simulation setting
    10.4.2  Results and analysis
  10.5  Case study: fault diagnosis of bearing-rotor system
  10.6  Conclusions
  10.7  References
Chapter 11  Oscillation-based permutation entropy calculation as dynamic nonlinear feature for health monitoring of rolling element bearing
  11.1  Introduction
  11.2  Weaknesses of PE in dynamic health monitoring
    11.2.1  Simulation model
    11.2.2  Two key weaknesses
  11.3  Oscillation-based permutation entropy
    11.3.1  Effect of bearing FSC on PE calculation
    11.3.2  Theory of oscillation based FSC separation scheme
    11.3.3  OBPE calculation for dynamic beating health monitoring
  11.4  Parameter selection for OBPE
    11.4.1  Selection of parameters related to TQWT
    11.4.2  Selection of data length

    11.4.3  Selection of embedding dimension and time delay
  11.5  Case study
  11.6  Conclusions
  11.7  References
Chapter 12  Summary

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