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Full Description
The intelligent diagnosis and maintenance of the machine mainly includes condition monitoring, fault diagnosis, performance degradation assessment and remaining useful life prediction, which plays an important role in protecting people's lives and property. In actual engineering scenarios, machine users always hope to use an automatic method to shorten the maintenance cycle and improve the accuracy of fault diagnosis and prognosis. In the past decade, Artificial Intelligence applications have flourished in many different fields, which also provide powerful tools for intelligent diagnosis and maintenance.
This book highlights the latest advances and trends in new generation artificial intelligence-driven techniques, including knowledge-driven deep learning, transfer learning, adversarial learning, complex network, graph neural network and multi-source information fusion, for diagnosis and maintenance of rotating machinery. Its primary focus is on the utilization of advanced artificial intelligence techniques to monitor, diagnose, and perform predictive maintenance of critical structures and machines, such as aero-engine, gas turbines, wind turbines, and machine tools.
The main markets of this book include academic and industrial fields, such as academic institutions, libraries of university, industrial research center. This book is essential reading for faculty members of university, graduate students, and industry professionals in the fields of diagnosis and maintenance.
Contents
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.
