New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques
portes grátis
New Generation Artificial Intelligence-Driven Diagnosis and Maintenance Techniques
Advanced Machine Learning Models, Methods and Applications
Huang, Xin; Wen, Guangrui; Chen, Xuefeng; Lei, Zihao
Springer Verlag, Singapore
10/2024
349
Dura
9789819711758
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Introduction.- Overview of Intelligent Fault Diagnosis and Maintenance for Rotating Machinery.- Deep Learning and Sparse Representation Coupled Intelligent Diagnosis and Maintenance.- Sparse Model-Driven Deep Learning for Weak Fault Diagnosis of Rolling Bearings.- Memory Residual Regression Autoencoder for Bearing Fault Detection.- Transfer Learning-based Intelligent Diagnosis and Maintenance.- Fault Diagnosis of Polytropic Conditions Based on Transfer Learning.- Performance Degradation Assessment Based on Transfer learning for Bearing.- Remaining Useful Life Prediction on .- Transfer Learning for Bearing.- Adversarial Learning-based Intelligent Diagnosis and Maintenance.- Deep Sequence Multi-distribution Adversarial Model for Abnormal Condition Detection in Industry.- Multi-Scale Lightweight Fault Diagnosis Model Based on Adversarial Learning.- Performance Degradation Assessment Based on Adversarial Learning for Bearing.- Graph-structured Information-based Intelligent Diagnosisand Maintenance.- Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis.- Community Clustering Algorithms and Its Application in Machine Fault Diagnosis.- Remaining Life Assessment of Rolling Bearing Based on Graph Neural Network.- Multi-source Information Fusion-based Intelligent Diagnosis and Maintenance.- Intelligent Fault Diagnosis Method Based on Multi-source Data and Multi-Feature Fusion.- D-S Evidence Theory and Its Application for Fault Diagnosis of Machinery.- Conclusion, Challenges, and Future Work.- Conclusion, Challenges, and Future Work.
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Prognostics Health Management (PHM);Abnormal Detection;Condition Monitoring;Fault Diagnosis and Prognosis;Remaining Useful Life Prediction;Deep Learning;Transfer Learning;Adversarial Learning;Complex Network;Information Fusion
Introduction.- Overview of Intelligent Fault Diagnosis and Maintenance for Rotating Machinery.- Deep Learning and Sparse Representation Coupled Intelligent Diagnosis and Maintenance.- Sparse Model-Driven Deep Learning for Weak Fault Diagnosis of Rolling Bearings.- Memory Residual Regression Autoencoder for Bearing Fault Detection.- Transfer Learning-based Intelligent Diagnosis and Maintenance.- Fault Diagnosis of Polytropic Conditions Based on Transfer Learning.- Performance Degradation Assessment Based on Transfer learning for Bearing.- Remaining Useful Life Prediction on .- Transfer Learning for Bearing.- Adversarial Learning-based Intelligent Diagnosis and Maintenance.- Deep Sequence Multi-distribution Adversarial Model for Abnormal Condition Detection in Industry.- Multi-Scale Lightweight Fault Diagnosis Model Based on Adversarial Learning.- Performance Degradation Assessment Based on Adversarial Learning for Bearing.- Graph-structured Information-based Intelligent Diagnosisand Maintenance.- Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis.- Community Clustering Algorithms and Its Application in Machine Fault Diagnosis.- Remaining Life Assessment of Rolling Bearing Based on Graph Neural Network.- Multi-source Information Fusion-based Intelligent Diagnosis and Maintenance.- Intelligent Fault Diagnosis Method Based on Multi-source Data and Multi-Feature Fusion.- D-S Evidence Theory and Its Application for Fault Diagnosis of Machinery.- Conclusion, Challenges, and Future Work.- Conclusion, Challenges, and Future Work.
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