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Reliability and Risk Assessment in Engineering : Proceedings of INCRS 2018 / edited by Vijay Kumar Gupta, Prabhakar V. Varde, P. K. Kankar, Narendra Joshi

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Lecture Notes in Mechanical Engineering | Springer eBook CollectionPublisher: Singapore : Springer Singapore, 2020Publisher: Singapore : Imprint: Springer, 2020Edition: 1st ed. 2020Description: 1 Online-Ressource(XXVI, 532 p. 226 illus., 144 illus. in color.)ISBN:
  • 9789811537462
Subject(s): Additional physical formats: 9789811537455 | 9789811537479 | 9789811537486 | Erscheint auch als: 9789811537455 Druck-Ausgabe | Erscheint auch als: 9789811537479 Druck-Ausgabe | Erscheint auch als: 9789811537486 Druck-AusgabeDDC classification:
  • 658.56 23
DOI: DOI: 10.1007/978-981-15-3746-2Online resources: Summary: Section 1: Big Data Analytics and Software Engineering -- Section 2: Data Analytics for Reliability: Applications -- Section 3: Condition Monitoring Techniques and Applications -- Section 4: Health Monitoring and Management using Multi-Sensors -- Section 5: Diagnosis and Prognosis of Mechanical Systems -- Section 6: Design for reliability -- Section 7: Optimization and Machine Learning Techniques for Industrial Applications -- Section 8: Performance/ Failure Analysis of Materials in Service -- Section 9: Reliability Issues in Electrical Distribution Systems.Summary: This volume is a collection of articles on reliability and safety engineering presented during INCRS 2018. The articles cover a variety of topics such as big data analytics and their applications in reliability assessment and condition monitoring, health monitoring, management, diagnostics and prognostics of mechanical systems, design for reliability and optimization, and machine learning for industrial applications. A special aspect of this volume is the coverage of performance, failure and reliability issues in electrical distribution systems. This book will be a useful reference for graduate students, researchers and professionals working in the area of reliability assessment, condition monitoring and predictive maintenance.PPN: PPN: 1699179522Package identifier: Produktsigel: ZDB-2-ENG | ZDB-2-SEB | ZDB-2-SXE
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