國立虎尾科技大學 |

New generation artificial intelligence-driven diagnosis and maintenance techniques = advanced machine learning models, methods and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	New generation artificial intelligence-driven diagnosis and maintenance techniques/ by Guangrui Wen ... [et al.].
Reminder of title:	advanced machine learning models, methods and applications /
other author:	Wen, Guangrui.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xvii, 349 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Industrial applications. -
Online resource:	https://doi.org/10.1007/978-981-97-1176-5
ISBN:	9789819711765

New generation artificial intelligence-driven diagnosis and maintenance techniques = advanced machine learning models, methods and applications /
New generation artificial intelligence-driven diagnosis and maintenance techniquesadvanced machine learning models, methods and applications /[electronic resource] :by Guangrui Wen ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xvii, 349 p. :ill. (some col.), digital ;24 cm. - Smart sensors, measurement and instrumentation,v. 462194-8410 ;. - Smart sensors, measurement and instrumentation ;v.8..

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.

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.

ISBN: 9789819711765

Standard No.: 10.1007/978-981-97-1176-5doiSubjects--Topical Terms:

796380
Artificial intelligence
--Industrial applications.

LC Class. No.: TA347.A78

Dewey Class. No.: 670.28563

New generation artificial intelligence-driven diagnosis and maintenance techniques = advanced machine learning models, methods and applications /
LDR:04315nam a2200337 a 4500 001 1154849
003 DE-He213
005 20240929130234.0
006 m d
007 cr nn 008maaau
008 250619s2024 si s 0 eng d
020 $a 9789819711765 $q (electronic bk.)
020 $a 9789819711758 $q (paper)
024 7 $a 10.1007/978-981-97-1176-5 $2 doi
035 $a 978-981-97-1176-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TA347.A78
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 670.28563 $2 23
090 $a TA347.A78 $b N532 2024
245 0 0 $a New generation artificial intelligence-driven diagnosis and maintenance techniques $h [electronic resource] : $b advanced machine learning models, methods and applications / $c by Guangrui Wen ... [et al.].
260 $a Singapore : $c 2024. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xvii, 349 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Smart sensors, measurement and instrumentation, $x 2194-8410 ; $v v. 46
505 0 $a 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.
520 $a 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.
650 0 $a Artificial intelligence $x Industrial applications. $3 796380
650 0 $a Machinery $x Maintenance and repair $x Data processing. $3 1482665
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Machines, Tools, Processes. $3 1366415
650 2 4 $a Security Science and Technology. $3 783419
700 1 $a Wen, Guangrui. $3 1482664
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Smart sensors, measurement and instrumentation ; $v v.8. $3 1023100
856 4 0 $u https://doi.org/10.1007/978-981-97-1176-5
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)