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عدد المساهمات : 18962 التقييم : 35392 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: كتاب Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems الثلاثاء 10 يناير 2023, 1:34 am | |
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أخواني في الله أحضرت لكم كتاب Big Data-Driven Intelligent Fault Diagnosis and Prognosis for Mechanical Systems Yaguo Lei , Naipeng Li , Xiang Li
و المحتوى كما يلي :
Contents 1 Introduction and Background . 1 1.1 Introduction . 1 1.1.1 AI Technologies for Data Processing 4 1.1.2 Big Data-Driven Intelligent Predictive Maintenance . 5 1.1.3 Big Data Analytics Platform Practices 6 1.2 Overview of Big Data-Driven PHM . 9 1.2.1 Data Acquisition . 9 1.2.2 Data Processing 11 1.2.3 Diagnosis . 12 1.2.4 Prognosis . 13 1.2.5 Maintenance 15 1.3 Preface to Book Chapters 16 References . 18 2 Conventional Intelligent Fault Diagnosis 21 2.1 Introduction . 21 2.2 Typical Neural Network-Based Methods . 23 2.2.1 Introduction to Neural Networks . 23 2.2.2 Intelligent Diagnosis Using Radial Basis Function Network 27 2.2.3 Intelligent Diagnosis Using Wavelet Neural Network 31 2.2.4 Epilog 37 2.3 Statistical Learning-Based Methods . 37 2.3.1 Introduction to Statistical Learning . 38 2.3.2 Intelligent Diagnosis Using Support Vector Machine 39 2.3.3 Intelligent Diagnosis Using Relevant Vector Machine . 49 2.3.4 Epilog 57 2.4 Conclusions . 57 References . 58 viiviii Contents 3 Hybrid Intelligent Fault Diagnosis . 61 3.1 Introduction . 61 3.2 Multiple WKNN Fault Diagnosis . 62 3.2.1 Motivation 62 3.2.2 Diagnosis Model Based on Combination of Multiple WKNN . 63 3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings 67 3.2.4 Epilog 69 3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis . 71 3.3.1 Motivation 71 3.3.2 Multiple ANFIS Combination with GA . 72 3.3.3 Fault Diagnosis Method Based on Multiple ANFIS Combination 73 3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings 75 3.3.5 Epilog 80 3.4 A Multidimensional Hybrid Intelligent Method . 81 3.4.1 Motivation 81 3.4.2 Multiple Classifier Combination 82 3.4.3 Diagnosis Method Based on Multiple Classifier Combination 84 3.4.4 Intelligent Diagnosis Case of Gearboxes . 87 3.4.5 Epilog 91 3.5 Conclusions . 91 References . 92 4 Deep Transfer Learning-Based Intelligent Fault Diagnosis . 95 4.1 Introduction . 95 4.2 Deep Belief Network for Few-Shot Fault Diagnosis . 98 4.2.1 Motivation 98 4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning 99 4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots 106 4.2.4 Epilog 110 4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data 111 4.3.1 Motivation 111 4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model 113 4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data 121 4.3.4 Epilog 125 4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis 126Contents ix 4.4.1 Motivation 126 4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model 127 4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry . 136 4.4.4 Epilog 142 4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis 144 4.5.1 Motivation 144 4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model . 146 4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets . 151 4.5.4 Epilog 161 4.6 Conclusions . 163 References . 164 5 Data-Driven RUL Prediction 167 5.1 Introduction . 167 5.2 Deep Separable Convolutional Neural Network-Based RUL Prediction . 169 5.2.1 Motivation 169 5.2.2 Deep Separable Convolutional Network . 169 5.2.3 Architecture of DSCN 170 5.2.4 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings . 173 5.2.5 Epilog 180 5.3 Recurrent Convolutional Neural Network-Based RUL Prediction . 181 5.3.1 Motivation 181 5.3.2 Recurrent Convolutional Neural Network 181 5.3.3 Architecture of RCNN 182 5.3.4 RUL Prediction Case Study of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings 188 5.3.5 Epilog 194 5.4 Multi-scale Convolutional Attention Network-Based RUL Prediction . 195 5.4.1 Motivation 195 5.4.2 Multi-scale Convolutional Attention Network 195 5.4.3 Architecture of MSCAN 196 5.4.4 RUL Prediction Case of a Life Testing of Milling Cutters . 202 5.4.5 Epilog 207 5.5 Conclusions . 208 References . 209x Contents 6 Data-Model Fusion RUL Prediction 213 6.1 Introduction . 213 6.2 RUL Prediction with Random Fluctuation Variability 215 6.2.1 Motivation 215 6.2.2 RUL Prediction Considering Random Fluctuation Variability 216 6.2.3 RUL Prediction Case of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings . 222 6.2.4 Epilog 227 6.3 RUL Prediction with Unit-to-Unit Variability . 227 6.3.1 Motivation 227 6.3.2 RUL Prediction Model Considering Unit-to-Unit Variability 229 6.3.3 RUL Prediction Case of Turbofan Engine Degradation Dataset . 237 6.3.4 Epilog 239 6.4 RUL Prediction with Time-Varying Operational Conditions 241 6.4.1 Motivation 241 6.4.2 RUL Prediction Model Considering Time-Varying Operational Conditions . 243 6.4.3 RUL Prediction Case of Accelerated Degradation Experiments of Thrusting Bearings . 252 6.4.4 Epilog 255 6.5 RUL Prediction with Dependent Competing Failure Processes 256 6.5.1 Motivation 256 6.5.2 RUL Prediction Model Considering Dependent Competing Failure Processes 258 6.5.3 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings . 270 6.5.4 Epilog 275 6.6 Conclusions . 275 References . 276 Glossary 279 About the Authors Glossary AACO Accumulative amplitudes of carrier orders AC Alternating current ADT Accelerated degradation test AE Acoustic emission AI Artificial intelligence ANN Artificial neural network AR Autoregressive ARE Absolute relative error ARMA Autoregressive moving average AUC Area under the receiver operation characteristic curve BFP Bearing fault in the planet gear BM Brownian motion BN Batch normalization CaAE Capsule auto-encoder CBM Condition-based maintenance CD Contrastive divergence CDET Compensation distance evaluation technique CDF Cumulative distribution function CLSTM Convolutional long short-term memory CNC Computer numerical control CNN Convolutional neural network CPG Crack in the planetary gear CS Crack in the sun gear CWRU Case Western Reserve University DAFD Domain adaptation for fault diagnosis DAN Deep adaptation network DAQ Data acquisition DBN Deep belief network DBNCL Deep belief network with continual learning DCFP Dependent competing failure process DCN Deep convolutional network DDC Deep domain confusion DDL Dynamic dense layer DNN Deep neural network DPS Degradation process simulation DPTLN Deep partial transfer learning network DSCN Deep separable convolutional network EEMD Ensemble empirical mode decomposition EM Expectation maximization EMD Empirical mode decomposition ERDS Energy ratio based on difference spectrum ERM Empirical risk minimization FCL Fully-connected layer FFT Fast Fourier transform FHT First hitting time FPT First predicting time FT Failure threshold GAN Generative adversarial network GAP Global average pooling GA Genetic algorithm GMP Global max pooling HI Health indicator HPP Homogeneous Poisson process i.i.d. Independent identically distributed IF Inner race failure ILWAL Instance-level weighted adversarial learning IMF Intrinsic mode function IoT Internet of Things KNN K nearest neighbor KKT Karush-Kuhn-Tucker KL Kullback-Leibler LOESS Locally weighted scatter smoothing method mAP Mean average precision MCNN Multi-scale convolutional neural network MLAN Multi-layer adaptation network MLP Multi-layer perceptron MMD Maximum mean discrepancy MPE Multi-scale displacement entropy MSCAN Multi-scale convolutional attention network MSE Mean square error MRVM Multiclass relevance vector machine OAA One-against-all OAO One-against-one OF Outer race failure OSVM Open set support vector machine PCA Principal component analysisGlossary 281 PDF Probability density function PE Permutation entropy PHM Prognostics and health management RBF Radial basis function RBM Restricted Boltzmann machine RCNN Recurrent convolutional neural network RDN Residual dense network ReLU Rectified linear unit ResNet Residual network RF Rolling element failure RKHS Reproducing Hilbert space RMS Root mean square RMSE Root mean square error RUL Remaining useful life RVM Relevance vector machine SD Standard deviation SE Squeeze and excitation SLT Statistical learning theory SNR Signal-to-noise ratio SRM Structural risk minimization SPRO Spectrum peak ratio of bearing outer race SPRI Spectrum peak ratio of bearing inner race SPRR Spectrum peak ratio of bearing roller STFT Short-time Fourier transform SVM Support vector machine SW Wear in the sun gear TCA Transfer component analysis TD Temporal dimension TSA Time synchronous average t-SNE T-distributed stochastic neighbor embedding UMLE Unit maximum likelihood estimation UtUV Unit-to-unit variability VC Vapnik-Chervonenkis WKNN Weighted K nearest neighbor WNN Wavelet neural network WPM Wiener process-based method WPT Wavelet packet transform WPTLS Wavelet packet transform with the lifting scheme WPW Wear in the planetary gear WS Wear in the sun gear
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