Localization of defects in rolling element bearings by dynamic classification based on meta-analysis of indicators: supervised real-time OPTICS method-Reference-Cited by-同舟云学术

Localization of defects in rolling element bearings by dynamic classification based on meta-analysis of indicators: supervised real-time OPTICS method

Published:2022-02-01 Issue:7 Volume:47 Page:9151-9171
ISSN:2193-567X
Container-title:Arabian Journal for Science and Engineering
language:en
Short-container-title:Arab J Sci Eng

Author:

Hamouche K.^ORCID,Rasolofondraibe L.,Chiementin X.,Felkaoui A.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

https://link.springer.com/content/pdf/10.1007/s13369-021-06528-0.pdf

Reference45 articles.

1. Guo, J.; Zhen, D.; Li, H.; Shi, Z.; Gu, F.; Ball, A.D.: Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method. Measurement 139, 226–235 (2019)

2. Chen, B., et al.: Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing. Meas. J. Int. Meas. Confed. 131, 400–411 (2019)

3. Syafrudin, M.; Alfian, G.; Fitriyani, N.L.; Rhee, J.: Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing. Sensors (Switzerland) 18(9), 2946 (2018)

4. Diaz-Rozo, J.; Bielza, C.; Larrañaga, P.: Machine learning-based CPS for clustering high throughput machining cycle conditions. Procedia Manuf. 10, 997–1008 (2017)

5. Smiti, A.; Elouedi, Z.: Dynamic DBSCAN-GM clustering algorithm. In: 16th IEEE International Symposium on Computational Intelligence and Informatics, pp. 311–316 (2015)

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