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Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024Volume 1 [Kõva köide]

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  • Formaat: Hardback, 754 pages, kõrgus x laius: 235x155 mm, 251 Illustrations, color; 52 Illustrations, black and white; X, 754 p. 303 illus., 251 illus. in color., 1 Hardback
  • Sari: Mechanisms and Machine Science 181
  • Ilmumisaeg: 22-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031933265
  • ISBN-13: 9783031933264
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Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences: UNIfied 2024Volume 1Suurem pilt
  • Formaat: Hardback, 754 pages, kõrgus x laius: 235x155 mm, 251 Illustrations, color; 52 Illustrations, black and white; X, 754 p. 303 illus., 251 illus. in color., 1 Hardback
  • Sari: Mechanisms and Machine Science 181
  • Ilmumisaeg: 22-Sep-2025
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031933265
  • ISBN-13: 9783031933264
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031933264.html

This volume gathers the latest advances, innovations and applications in the field of condition monitoring, damage assessment and maintenance engineering, as presented by leading international researchers and engineers at the UNIfied Conference of DAMAS (International Conference on Damage Assessment of Structures), IncoME  (International Conference on Maintenance Engineering) and TEPEN (The Efficiency and Performance Engineering), held in Jaipur, India on November 26-28, 2024. Topics include sensors and measurement systems, condition monitoring and predictive maintenance, machine health monitoring, maintenance organisation & performance measurement, Industrial Internet of Things (IIoT), cyber physical systems, machine learning in maintenance and production environment, plant maintenance, asset management, reliability, artificial intelligence and related areas, life cycle cost optimisation, health management. The contributions, which were selected through a rigorous internationalpeer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.

Artificial Intelligence to Measure Idle Time of an Assembly Line
Worker.- Predictive Modeling of Surface Roughness in Abrasive Machining
Operation Using Machine Learning.- Biometric based Secured Voting System for
Election.- Bearing Fault Diagnosis and Generalization Error Analysis Based on
Transfer Learning.- Framework for a Non-Intrusive Monitoring Solution for
Independent Elderly.- Machine Learning-Driven Clustering Based Environmental,
Social, and Governance Performance Prediction Model.