國立虎尾科技大學 |

Artificial intelligence for safety and reliability engineering = methods, applications, and challenges /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial intelligence for safety and reliability engineering/ edited by Kim Phuc Tran.
Reminder of title:	methods, applications, and challenges /
other author:	Tran, Kim Phuc.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	v, 199 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Manufacturing processes - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-71495-5
ISBN:	9783031714955

Artificial intelligence for safety and reliability engineering = methods, applications, and challenges /
Artificial intelligence for safety and reliability engineeringmethods, applications, and challenges /[electronic resource] :edited by Kim Phuc Tran. - Cham :Springer Nature Switzerland :2024. - v, 199 p. :ill., digital ;24 cm. - Springer series in reliability engineering,2196-999X. - Springer series in reliability engineering..

Introduction to Artificial Intelligence for Safety and Reliability Engineering -- Artificial Intelligence for Safety and Reliability Engineering in Industry 5.0 Methods, Applications and Challenges -- System Reliability Inference for Common Cause Failure Model in Contexts of Missing Information -- Predictive maintenance enabled by a Light Weight Federated Learning in Smart Manufacturing: Remaining Useful Lifetime Prediction -- Explainable Trustworthy and Transparent Artificial Intelligence for Reliability Engineering and Safety Applications -- Inverse Reinforcement Learning for Predictive Maintenance -- Reliability and Risk Assessment with Explainable Artificial Intelligence -- An Anomaly Detection Framework for Safety and Reliability Engineering -- Wearable Technology for Workplace Safety with Embedded Artificial Intelligence -- Safety and Reliability of Artificial Intelligence systems -- Physics-informed machine learning for reliability and systems safety applications.

This book is a comprehensive exploration of the latest theoretical research, technological advancements, and real-world applications of artificial intelligence (AI) for safety and reliability engineering. Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision-makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.

ISBN: 9783031714955

Standard No.: 10.1007/978-3-031-71495-5doiSubjects--Topical Terms:

636667
Manufacturing processes
--Data processing.

LC Class. No.: TS183 / .A78 2024

Dewey Class. No.: 670.28563

Artificial intelligence for safety and reliability engineering = methods, applications, and challenges /
LDR:02800nam a2200337 a 4500 001 1154765
003 DE-He213
005 20240928131728.0
006 m d
007 cr nn 008maaau
008 250619s2024 sz s 0 eng d
020 $a 9783031714955 $q (electronic bk.)
020 $a 9783031714948 $q (paper)
024 7 $a 10.1007/978-3-031-71495-5 $2 doi
035 $a 978-3-031-71495-5
040 $a GP $c GP
041 0 $a eng
050 4 $a TS183 $b .A78 2024
072 7 $a TGP $2 bicssc
072 7 $a TEC009060 $2 bisacsh
072 7 $a TGP $2 thema
082 0 4 $a 670.28563 $2 23
090 $a TS183 $b .A791 2024
245 0 0 $a Artificial intelligence for safety and reliability engineering $h [electronic resource] : $b methods, applications, and challenges / $c edited by Kim Phuc Tran.
260 $a Cham : $c 2024. $b Springer Nature Switzerland : $b Imprint: Springer,
300 $a v, 199 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in reliability engineering, $x 2196-999X
505 0 $a Introduction to Artificial Intelligence for Safety and Reliability Engineering -- Artificial Intelligence for Safety and Reliability Engineering in Industry 5.0 Methods, Applications and Challenges -- System Reliability Inference for Common Cause Failure Model in Contexts of Missing Information -- Predictive maintenance enabled by a Light Weight Federated Learning in Smart Manufacturing: Remaining Useful Lifetime Prediction -- Explainable Trustworthy and Transparent Artificial Intelligence for Reliability Engineering and Safety Applications -- Inverse Reinforcement Learning for Predictive Maintenance -- Reliability and Risk Assessment with Explainable Artificial Intelligence -- An Anomaly Detection Framework for Safety and Reliability Engineering -- Wearable Technology for Workplace Safety with Embedded Artificial Intelligence -- Safety and Reliability of Artificial Intelligence systems -- Physics-informed machine learning for reliability and systems safety applications.
520 $a This book is a comprehensive exploration of the latest theoretical research, technological advancements, and real-world applications of artificial intelligence (AI) for safety and reliability engineering. Smart manufacturing relies on predictive maintenance (PdM) to ensure sustainable production systems, and the integration of AI has become increasingly prevalent in this field. This book serves as a valuable resource for researchers, practitioners, and decision-makers in manufacturing. By combining theoretical research, practical applications, and case studies, it equips readers with the necessary knowledge and tools to implement AI for safety and reliability engineering effectively in smart manufacturing contexts.
650 0 $a Manufacturing processes $x Data processing. $3 636667
650 0 $a Artificial intelligence $x Industrial applications. $3 796380
650 1 4 $a Industrial and Production Engineering. $3 593943
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Production. $3 1111231
700 1 $a Tran, Kim Phuc. $e editor. $1 https://orcid.org/0000-0002-6005-1497 $3 1391128
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Springer series in reliability engineering. $3 632430
856 4 0 $u https://doi.org/10.1007/978-3-031-71495-5
950 $a Engineering (SpringerNature-11647)