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Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering 2023 ed. [Hardback]

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  • Formāts: Hardback, 1102 pages, height x width: 235x155 mm, weight: 2014 g, 571 Illustrations, color; 138 Illustrations, black and white; XIV, 1102 p. 709 illus., 571 illus. in color. In 2 volumes, not available separately., 2 hardbacks
  • Sērija : Mechanisms and Machine Science 117
  • Izdošanas datums: 18-Sep-2022
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030990745
  • ISBN-13: 9783030990749
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  • Hardback
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  • Formāts: Hardback, 1102 pages, height x width: 235x155 mm, weight: 2014 g, 571 Illustrations, color; 138 Illustrations, black and white; XIV, 1102 p. 709 illus., 571 illus. in color. In 2 volumes, not available separately., 2 hardbacks
  • Sērija : Mechanisms and Machine Science 117
  • Izdošanas datums: 18-Sep-2022
  • Izdevniecība: Springer Nature Switzerland AG
  • ISBN-10: 3030990745
  • ISBN-13: 9783030990749
Citas grāmatas par šo tēmu:

This volume gathers the latest advances, innovations and applications in the field of condition monitoring, plant maintenance and reliability, as presented by leading international researchers and engineers at the 6th International Conference on Maintenance Engineering and the 2021 conference of the Efficiency and Performance Engineering Network (IncoME-VI  TEPEN 2021), held in Tianjin, China on October 20-23, 2021. Topics include vibro-acoustics monitoring, condition-based maintenance, sensing and instrumentation, machine health monitoring, maintenance auditing and organization, non-destructive testing, reliability, asset management, condition monitoring, life-cycle cost optimisation, prognostics and health management, maintenance performance measurement, manufacturing process monitoring, and robot-based monitoring and diagnostics. The contributions, which were selected through a rigorous international peer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.

Chapter 1: An Optical Gyroscope Based Technique for
Calibrating Angular-Measuring Instrument .
Chapter 2: Data Analysis for
Predictive Maintenance Using Time Series and Deep Learning Models A Case
Study in a Pulp Paper Industry.
Chapter 3: Reinforcement Learning Fault
Diagnosis Method Based on Less Tag Data.
Chapter 4: Optimization  design 
and  simulation  analysis  of miniature boring machine based on ADAMS.