Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Lei, Yaguo (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Oxford, United Kingdom : Elsevier, 2017.
Temas:
Electric machinery > Rotors.
Fault location (Engineering)
Expert systems (Computer science)
D�etection de d�efaut (Ing�enierie)
Syst�emes experts (Informatique)
TECHNOLOGY & ENGINEERING > Mechanical.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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