Big-data driven intelligent fault diagnosis and prognosis for mechanical systems / / Yaguo Lei, Naipeng Li, Xiang Li

• Record Nr.: UNINA9910627272303321
• Autore: Lei Yaguo
• Pubbl/distr/stampa: Singapore : , : Xi'an Jiaotong University Press : , : Springer, , [2023]; ©2023
• ISBN: 981-16-9131-2
• Descrizione fisica: 1 online resource (292 pages)
• Disciplina: 005.7
• Soggetti: Big data; Fault location (Engineering); Mechanical engineering - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Contents -- About the Authors -- 1 Introduction and Background -- 1.1 Introduction -- 1.1.1 AI Technologies for Data Processing -- 1.1.2 Big Data-Driven Intelligent Predictive Maintenance -- 1.1.3 Big Data Analytics Platform Practices -- 1.2 Overview of Big Data-Driven PHM -- 1.2.1 Data Acquisition -- 1.2.2 Data Processing -- 1.2.3 Diagnosis -- 1.2.4 Prognosis -- 1.2.5 Maintenance -- 1.3 Preface to Book Chapters -- References -- 2 Conventional Intelligent Fault Diagnosis -- 2.1 Introduction -- 2.2 Typical Neural Network-Based Methods -- 2.2.1 Introduction to Neural Networks -- 2.2.2 Intelligent Diagnosis Using Radial Basis Function Network -- 2.2.3 Intelligent Diagnosis Using Wavelet Neural Network -- 2.2.4 Epilog -- 2.3 Statistical Learning-Based Methods -- 2.3.1 Introduction to Statistical Learning -- 2.3.2 Intelligent Diagnosis Using Support Vector Machine -- 2.3.3 Intelligent Diagnosis Using Relevant Vector Machine -- 2.3.4 Epilog -- 2.4 Conclusions -- References -- 3 Hybrid Intelligent Fault Diagnosis -- 3.1 Introduction -- 3.2 Multiple WKNN Fault Diagnosis -- 3.2.1 Motivation -- 3.2.2 Diagnosis Model Based on Combination of Multiple WKNN -- 3.2.3 Intelligent Diagnosis Case Study of Rolling Element Bearings -- 3.2.4 Epilog -- 3.3 Multiple ANFIS Hybrid Intelligent Fault Diagnosis -- 3.3.1 Motivation -- 3.3.2 Multiple ANFIS Combination with GA -- 3.3.3 Fault Diagnosis Method Based ; on Multiple ANFIS Combination -- 3.3.4 Intelligent Diagnosis Case of Rolling Element Bearings -- 3.3.5 Epilog -- 3.4 A Multidimensional Hybrid Intelligent Method -- 3.4.1 Motivation -- 3.4.2 Multiple Classifier Combination -- 3.4.3 Diagnosis Method Based on Multiple Classifier Combination -- 3.4.4 Intelligent Diagnosis Case of Gearboxes -- 3.4.5 Epilog -- 3.5 Conclusions -- References -- 4 Deep Transfer Learning-Based Intelligent Fault Diagnosis.; 4.1 Introduction -- 4.2 Deep Belief Network for Few-Shot Fault Diagnosis -- 4.2.1 Motivation -- 4.2.2 Deep Belief Network-Based Diagnosis Model with Continual Learning -- 4.2.3 Few-Shot Fault Diagnosis Case of Industrial Robots -- 4.2.4 Epilog -- 4.3 Multi-Layer Adaptation Network for Fault Diagnosis with Unlabeled Data -- 4.3.1 Motivation -- 4.3.2 Multi-Layer Adaptation Network-Based Diagnosis Model -- 4.3.3 Fault Diagnosis Case of Locomotive Bearings with Unlabeled Data -- 4.3.4 Epilog -- 4.4 Deep Partial Adaptation Network for Domain-Asymmetric Fault Diagnosis -- 4.4.1 Motivation -- 4.4.2 Deep Partial Transfer Learning Net-Based Diagnosis Model -- 4.4.3 Partial Transfer Diagnosis of Gearboxes with Domain Asymmetry -- 4.4.4 Epilog -- 4.5 Instance-Level Weighted Adversarial Learning for Open-Set Fault Diagnosis -- 4.5.1 Motivation -- 4.5.2 Instance-Level Weighted Adversarial Learning-Based Diagnosis Model -- 4.5.3 Fault Diagnosis Case of Rolling Bearing Datasets -- 4.5.4 Epilog -- 4.6 Conclusions -- References -- 5 Data-Driven RUL Prediction -- 5.1 Introduction -- 5.2 Deep Separable Convolutional Neural Network-Based RUL Prediction -- 5.2.1 Motivation -- 5.2.2 Deep Separable Convolutional Network -- 5.2.3 Architecture of DSCN -- 5.2.4 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings -- 5.2.5 Epilog -- 5.3 Recurrent Convolutional Neural Network-Based RUL Prediction -- 5.3.1 Motivation -- 5.3.2 Recurrent Convolutional Neural Network -- 5.3.3 Architecture of RCNN -- 5.3.4 RUL Prediction Case Study of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings -- 5.3.5 Epilog -- 5.4 Multi-scale Convolutional Attention Network-Based RUL Prediction -- 5.4.1 Motivation -- 5.4.2 Multi-scale Convolutional Attention Network -- 5.4.3 Architecture of MSCAN.; 5.4.4 RUL Prediction Case of a Life Testing of Milling Cutters -- 5.4.5 Epilog -- 5.5 Conclusions -- References -- 6 Data-Model Fusion RUL Prediction -- 6.1 Introduction -- 6.2 RUL Prediction with Random Fluctuation Variability -- 6.2.1 Motivation -- 6.2.2 RUL Prediction Considering Random Fluctuation Variability -- 6.2.3 RUL Prediction Case of FEMTO-ST Accelerated Degradation Tests of Rolling Element Bearings -- 6.2.4 Epilog -- 6.3 RUL Prediction with Unit-to-Unit Variability -- 6.3.1 Motivation -- 6.3.2 RUL Prediction Model Considering Unit-to-Unit Variability -- 6.3.3 RUL Prediction Case of Turbofan Engine Degradation Dataset -- 6.3.4 Epilog -- 6.4 RUL Prediction with Time-Varying Operational Conditions -- 6.4.1 Motivation -- 6.4.2 RUL Prediction Model Considering Time-Varying Operational Conditions -- 6.4.3 RUL Prediction Case of Accelerated Degradation Experiments of Thrusting Bearings -- 6.4.4 Epilog -- 6.5 RUL Prediction with Dependent Competing Failure Processes -- 6.5.1 Motivation -- 6.5.2 RUL Prediction Model Considering Dependent Competing Failure Processes -- 6.5.3 RUL Prediction Case of Accelerated Degradation Experiments of Rolling Element Bearings -- 6.5.4 Epilog -- 6.6 Conclusions -- References -- Glossary.