Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery
Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group.
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| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier Science & Technology
2016
Butterworth-Heinemann |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780128115343 0128115343 |
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| Abstract | Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. |
|---|---|
| AbstractList | Provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The main contents include multi-domain signal processing and feature extraction, intelligent diagnosis models, clustering algorithms, hybrid intelligent diagnosis strategies, and RUL prediction approaches, etc. This book presents fundamental theories and advanced methods of identifying the occurrence, locations, and degrees of faults, and also includes information on how to predict the RUL of rotating machinery. Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. |
| Author | Lei, Yaguo |
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| Edition | 1 |
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| PublicationDate | 2016 2016-11-02 |
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| Snippet | Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery provides a comprehensive introduction of intelligent fault diagnosis and... Provides a comprehensive introduction of intelligent fault diagnosis and RUL prediction based on the current achievements of the author's research group. The... |
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| SubjectTerms | Machinery |
| TableOfContents | Cover -- Title page -- Copyright page -- Table of Contents -- Preface -- About the Author -- 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 Analysis -- 2.3.2 - 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 Transform -- 2.5.4 - 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 6.3.4 - Paris-Erdogan Model-Based Methods -- 6.3.4.1 - Motivation -- 6.3.4.2 - Health Indicator Construction -- 6.3.4.3 - RUL Prediction -- 6.3.4.4 - RUL Prediction of Rolling Element Bearings: a Case Study -- 6.3.5 - Epilog -- 6.4 - Conclusions -- References -- Glossary -- Index -- Back cover 3.4.2 - Stacked Tied-Weight Autoencoder-Based Fault Diagnosis -- 3.4.2.1 - Introduction -- 3.4.2.2 - Tied-Weight Autoencoder -- 3.4.2.3 - Deep Neural Network Using Stacked Tied-Weight Autoencoders -- 3.4.2.4 - Fault Diagnosis Method Based on Deep Neural Networks -- 3.4.2.5 - Intelligent Fault Diagnosis of Bearings Under Various Loads: A Case Study -- 3.4.2.6 - Epilog -- 3.4.3 - Sparse Coding-Based Fault Diagnosis -- 3.4.3.1 - Introduction -- 3.4.3.2 - Sparse Coding -- 3.4.3.3 - Softmax Regression -- 3.4.3.4 - Diagnosis Method Based on Sparse Coding -- 3.4.3.5 - Intelligent Fault Diagnosis of Bearings: A Locomotive Bearing Case Study -- 3.4.3.6 - Epilog -- 3.5 - Conclusions -- References -- 4 - Clustering algorithm-based fault diagnosis -- 4.1 - Introduction to Clustering Algorithm -- 4.2 - Weighted K Nearest Neighbor-Based Fault Diagnosis -- 4.2.1 - Introduction -- 4.2.2 - K Nearest Neighbor Algorithm -- 4.2.3 - Weighted K Nearest Neighbor Algorithm -- 4.2.4 - Diagnosis Method Based on WKNN -- 4.2.5 - Intelligent Diagnosis of Gear Crack Level: A Case Study -- 4.2.6 - Epilog -- 4.3 - Weighted Fuzzy c-Means-Based Fault Diagnosis -- 4.3.1 - Introduction -- 4.3.2 - K-Means Algorithm -- 4.3.3 - FCM Algorithm -- 4.3.4 - Weighted FCM Algorithm -- 4.3.5 - Diagnosis Method Based on WFCM -- 4.3.6 - Intelligent Diagnosis of Incipient Faults: A Rolling Element Bearing Case Study -- 4.3.7 - Epilog -- 4.4 - Hybrid Clustering Algorithm-Based Fault Diagnosis -- 4.4.1 - Introduction -- 4.4.2 - Feature Weight Calculation Based on ANNs -- 4.4.3 - Sample Weight Computation With a Distribution Density Function -- 4.4.4 - Cluster Number Determination -- 4.4.5 - Diagnosis Method Using the Hybrid Clustering Algorithm -- 4.4.6 - Intelligent Diagnosis of Compound Faults: A Locomotive Bearing Case Study -- 4.4.7 - Epilog -- 4.5 - Conclusions -- References 5 - Hybrid intelligent fault diagnosis methods -- 5.1 - Introduction -- 5.2 - Multiple WKNN Combination-Based Fault Diagnosis -- 5.2.1 - Motivation -- 5.2.2 - Multiple WKNN Combination -- 5.2.3 - Diagnosis Method Based on Multiple WKNN Combination -- 5.2.4 - Intelligent Diagnosis of Fault Categories: A Rolling Element Bearing Case Study -- 5.2.5 - Epilog -- 5.3 - Multiple ANFIS Hybrid Intelligent Fault Diagnosis -- 5.3.1 - Motivation -- 5.3.2 - Multiple ANFIS Combination with GAs -- 5.3.3 - Fault Diagnosis Method Based on Multiple ANFIS Combination -- 5.3.4 - Intelligent Diagnosis of Fault Severities: A Rolling Element Bearing Case Study -- 5.3.5 - Epilog -- 5.4 - A Multidimensional Hybrid Intelligent Method -- 5.4.1 - Motivation -- 5.4.2 - Multiple Classifier Combination -- 5.4.3 - Diagnosis Method Based on Multiple Classifier Combination -- 5.4.4 - Intelligent Diagnosis of Damage Categories: A Gearbox Case Study -- 5.4.5 - Epilog -- 5.5 - Conclusions -- References -- 6 - Remaining useful life prediction -- 6.1 - Background -- 6.2 - Data-driven Prediction Methods -- 6.2.1 - Introduction -- 6.2.2 - RVM-Based Prediction Methods -- 6.2.2.1 - RVM-Based Predictors -- 6.2.2.2 - Particle Filtering Algorithms -- 6.2.2.3 - Multikernel RVM-Based Prediction Method -- 6.2.2.4 - RUL Prediction of Gears: a Case Study -- 6.2.3 - Epilog -- 6.3 - Model-Based Prediction Methods -- 6.3.1 - Introduction -- 6.3.2 - Exponential Model-Based Methods -- 6.3.2.1 - Motivation -- 6.3.2.2 - Exponential Models -- 6.3.2.3 - An Improved Exponential Model-Based RUL Prediction Method -- 6.3.2.4 - RUL Prediction of Rolling Element Bearings: a Case Study -- 6.3.3 - Polynomial Model-Based Methods -- 6.3.3.1 - Motivation -- 6.3.3.2 - A Polynomial Model-Based RUL Prediction Method -- 6.3.3.3 - Simulation Verification -- 6.3.3.4 - RUL Prediction of Gearboxes: a Case Study 3.2.1.3 - Speeding up the Backpropagation -- 3.2.1.4 - Epilog -- 3.2.2 - 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 Method -- 3.2.3.5 - 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 -- 3.3.2.2 - Support Vector Machine -- 3.3.2.3 - Multiclass SVM -- 3.3.2.4 - Fault Diagnosis Method Based on SVM -- 3.3.2.5 - Intelligent Diagnosis of Bearing Faults: A Motor Bearing Case Study -- 3.3.2.6 - Epilog -- 3.3.3 - Relevant Vector Machine-Based Intelligent Fault Diagnosis -- 3.3.3.1 - Introduction -- 3.3.3.2 - Relevance Vector Machine -- 3.3.3.3 - Multiclass Relevance Vector Machine -- 3.3.3.4 - Intelligent Diagnosis Based on MRVM -- 3.3.3.5 - Intelligent Diagnosis of Planetary Gearboxes: A Case Study -- 3.3.3.6 - Epilog -- 3.4 - Deep Learning -- 3.4.1 - Introduction to Deep Learning |
| Title | Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery |
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