Proceedings of the UNIfied Conference of DAMAS, IncoME VIII and TEPEN Conferences
UNIfied 2024–Volume 2
Series: Mechanisms and Machine Science; 185;
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 14 October 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031959622
- Binding Hardback
- No. of pages675 pages
- Size 235x155 mm
- Language English
- Illustrations IX, 675 p. 243 illus., 218 illus. in color. Illustrations, black & white 700
Categories
Long description:
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.
MoreTable of Contents:
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.
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