Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks-Reference-Cited by-同舟云学术

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Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks

Published:2024-07-23 Issue: Volume:11 Page:1-6
ISSN:
Container-title:2024 IEEE Sensors Applications Symposium (SAS)
language:
Short-container-title:

Author:

Jansen Kai¹,Shallari Irida¹,Mourad Safer²,Werheit Patrick³,Bader Sebastian¹

Affiliation:

1. Mid Sweden University,Department of Computer and Electrical Engineering,Sundsvall,Sweden

2. Saurer Business Development Office & R&D Sensor BU SPIN, Saurer Intelligent Technology AG,Arbon,Switzerland

3. Saurer Machine Data Analytics, Saurer Spinning Solutions GmbH & Co. KG,Übach-Palenberg,Germany

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10636309/10636356/10636697.pdf?arnumber=10636697

Reference10 articles.

1. An intelligent detection and assessment method based on textile fabric image feature

2. Residual-life distributions from component degradation signals: A Bayesian approach

3. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery

4. Automation, Monitoring, and Standardization of Cell Product Manufacturing

5. A comparative analysis of air-jet yarn properties with the properties of ring spun yarns

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