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The Resource Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Yaguo Lei

Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Yaguo Lei

Label
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Title
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Statement of responsibility
Yaguo Lei
Creator
Author
Subject
Language
eng
Member of
Cataloging source
IDEBK
http://library.link/vocab/creatorName
Lei, Yaguo
Dewey number
621.31042
Index
no index present
LC call number
TJ1058
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/subjectName
  • Electric machinery
  • Fault location (Engineering)
  • Expert systems (Computer science)
Label
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Yaguo Lei
Instantiates
Publication
Bibliography note
Includes bibliographical references at the end of each chapters and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Title page; Copyright page; Table of Contents; About the Author; Preface; 1 -- Introduction and background; 1.1 -- Introduction; 1.2 -- Overview of PHM; 1.2.1 -- Data Acquisition; 1.2.2 -- Signal Processing; 1.2.3 -- Diagnostics; 1.2.4 -- Prognostics; 1.2.5 -- Maintenance Decision; 1.3 -- Preface to Book Chapters; References; 2 -- Signal processing and feature extraction; 2.1 -- Introduction; 2.2 -- Signal Preprocessing; 2.2.1 -- Trend Removal; 2.2.2 -- Signal Filtering; 2.3 -- Signal Processing in the Time Domain; 2.3.1 -- Correlation Analysis; 2.3.1.1 -- Autocorrelation Analysis
  • 2.3.1.2 -- Cross-Correlation Analysis2.3.2 -- Common Statistical Features in the Time Domain; 2.4 -- Signal Processing in the Frequency Domain; 2.4.1 -- Fourier Transform; 2.4.1.1 -- Fourier Series; 2.4.1.2 -- Fourier Integral Transform; 2.4.1.3 -- Discrete Fourier Transform; 2.4.1.4 -- Fast Fourier Transform; 2.4.2 -- Common Statistical Features in the Frequency Domain; 2.5 -- Signal Processing in the Time-Frequency Domain; 2.5.1 -- Short-Time Fourier Transform; 2.5.2 -- Wigner-Ville Distribution; 2.5.3 -- Wavelet Analysis; 2.5.3.1 -- Wavelet Transform; 2.5.3.2 -- Wavelet Basis and Fast Pyramidal Algorithm
  • 2.5.3.3 -- Wavelet Packet Transform2.5.4 -- Hilbert-Huang Transform; 2.5.4.1 -- Empirical Mode Decomposition; 2.5.4.2 -- Ensemble Empirical Mode Decomposition; 2.5.4.3 -- Hilbert Transform; 2.5.5 -- Common Feature Extraction in the Time-Frequency Domain; 2.6 -- Conclusions; References; 3 -- Individual intelligent method-based fault diagnosis; 3.1 -- Introduction to Intelligent Diagnosis Methods; 3.2 -- Artificial Neural Networks; 3.2.1 -- Introduction to Artificial Neural Networks; 3.2.1.1 -- Architecture of Neural Networks; 3.2.1.2 -- Backpropagation Algorithm; 3.2.1.3 -- Speeding up the Backpropagation
  • 3.2.1.4 -- Epilog3.2.2 -- Radial Basis Function Network-Based Fault Diagnosis; 3.2.2.1 -- Introduction; 3.2.2.2 -- Radial Basis Function Network; 3.2.2.3 -- Fault Diagnosis Method Based on RBF Network; 3.2.2.4 -- Intelligent Diagnosis of Bearing Faults: An Experimental Case Study; 3.2.2.5 -- Intelligent Diagnosis of Rub Faults: A Heavy Oil Catalytic Cracking Unit Case Study; 3.2.2.6 -- Epilog; 3.2.3 -- Wavelet Neural Network-Based Fault Diagnosis; 3.2.3.1 -- Introduction; 3.2.3.2 -- Wavelet Neural Network; 3.2.3.3 -- Sensitive IMF Selection and Feature Extraction
  • 3.2.3.4 -- WNN-Based Fault Diagnosis Method3.2.3.5 -- Intelligent Diagnosis of the Compound Faults: A Bearing Case Study; 3.2.3.6 -- Epilog; 3.2.4 -- Adaptive Neuro-Fuzzy Inference System-Based Fault Diagnosis; 3.2.4.1 -- Introduction; 3.2.4.2 -- Adaptive Neuro-Fuzzy Inference System; 3.2.4.3 -- Diagnosis Method With Multisensor Data Fusion; 3.2.4.4 -- Intelligent Diagnosis of Gear Faults: A Planetary Gearbox Case Study; 3.2.4.5 -- Epilog; 3.3 -- Statistical Learning Theory; 3.3.1 -- Introduction to Statistical Learning Theory; 3.3.2 -- Support Vector Machine-Based Fault Diagnosis Method
  • 3.3.2.1 -- Introduction
Extent
1 online resource.
Form of item
online
Isbn
9780128115350
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Specific material designation
remote
Label
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery, Yaguo Lei
Publication
Bibliography note
Includes bibliographical references at the end of each chapters and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Cover; Title page; Copyright page; Table of Contents; About the Author; Preface; 1 -- Introduction and background; 1.1 -- Introduction; 1.2 -- Overview of PHM; 1.2.1 -- Data Acquisition; 1.2.2 -- Signal Processing; 1.2.3 -- Diagnostics; 1.2.4 -- Prognostics; 1.2.5 -- Maintenance Decision; 1.3 -- Preface to Book Chapters; References; 2 -- Signal processing and feature extraction; 2.1 -- Introduction; 2.2 -- Signal Preprocessing; 2.2.1 -- Trend Removal; 2.2.2 -- Signal Filtering; 2.3 -- Signal Processing in the Time Domain; 2.3.1 -- Correlation Analysis; 2.3.1.1 -- Autocorrelation Analysis
  • 2.3.1.2 -- Cross-Correlation Analysis2.3.2 -- Common Statistical Features in the Time Domain; 2.4 -- Signal Processing in the Frequency Domain; 2.4.1 -- Fourier Transform; 2.4.1.1 -- Fourier Series; 2.4.1.2 -- Fourier Integral Transform; 2.4.1.3 -- Discrete Fourier Transform; 2.4.1.4 -- Fast Fourier Transform; 2.4.2 -- Common Statistical Features in the Frequency Domain; 2.5 -- Signal Processing in the Time-Frequency Domain; 2.5.1 -- Short-Time Fourier Transform; 2.5.2 -- Wigner-Ville Distribution; 2.5.3 -- Wavelet Analysis; 2.5.3.1 -- Wavelet Transform; 2.5.3.2 -- Wavelet Basis and Fast Pyramidal Algorithm
  • 2.5.3.3 -- Wavelet Packet Transform2.5.4 -- Hilbert-Huang Transform; 2.5.4.1 -- Empirical Mode Decomposition; 2.5.4.2 -- Ensemble Empirical Mode Decomposition; 2.5.4.3 -- Hilbert Transform; 2.5.5 -- Common Feature Extraction in the Time-Frequency Domain; 2.6 -- Conclusions; References; 3 -- Individual intelligent method-based fault diagnosis; 3.1 -- Introduction to Intelligent Diagnosis Methods; 3.2 -- Artificial Neural Networks; 3.2.1 -- Introduction to Artificial Neural Networks; 3.2.1.1 -- Architecture of Neural Networks; 3.2.1.2 -- Backpropagation Algorithm; 3.2.1.3 -- Speeding up the Backpropagation
  • 3.2.1.4 -- Epilog3.2.2 -- Radial Basis Function Network-Based Fault Diagnosis; 3.2.2.1 -- Introduction; 3.2.2.2 -- Radial Basis Function Network; 3.2.2.3 -- Fault Diagnosis Method Based on RBF Network; 3.2.2.4 -- Intelligent Diagnosis of Bearing Faults: An Experimental Case Study; 3.2.2.5 -- Intelligent Diagnosis of Rub Faults: A Heavy Oil Catalytic Cracking Unit Case Study; 3.2.2.6 -- Epilog; 3.2.3 -- Wavelet Neural Network-Based Fault Diagnosis; 3.2.3.1 -- Introduction; 3.2.3.2 -- Wavelet Neural Network; 3.2.3.3 -- Sensitive IMF Selection and Feature Extraction
  • 3.2.3.4 -- WNN-Based Fault Diagnosis Method3.2.3.5 -- Intelligent Diagnosis of the Compound Faults: A Bearing Case Study; 3.2.3.6 -- Epilog; 3.2.4 -- Adaptive Neuro-Fuzzy Inference System-Based Fault Diagnosis; 3.2.4.1 -- Introduction; 3.2.4.2 -- Adaptive Neuro-Fuzzy Inference System; 3.2.4.3 -- Diagnosis Method With Multisensor Data Fusion; 3.2.4.4 -- Intelligent Diagnosis of Gear Faults: A Planetary Gearbox Case Study; 3.2.4.5 -- Epilog; 3.3 -- Statistical Learning Theory; 3.3.1 -- Introduction to Statistical Learning Theory; 3.3.2 -- Support Vector Machine-Based Fault Diagnosis Method
  • 3.3.2.1 -- Introduction
Extent
1 online resource.
Form of item
online
Isbn
9780128115350
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Specific material designation
remote

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