Multiple feature vectors based fault classification for WSN integrated bearing of rolling mill

DOI10.1155/2018/3041591zbMath1403.93137OpenAlexW2795247863WikidataQ130042478 ScholiaQ130042478MaRDI QIDQ1629491

Heng Yin, Yan Qin, Luyang Zhang, Bo Qin

Publication date: 12 December 2018

Published in: Journal of Control Science and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2018/3041591

zbMATH Keywords

extreme learning machinebearingrolling millfault classification modelingfrequency index

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Production models (90B30) Application models in control theory (93C95) Frequency-response methods in control theory (93C80)

Cites Work

• Time-varying fault diagnosis for asynchronous multisensor systems based on augmented IMM and strong tracking filtering
• WOS-ELM-based double redundancy fault diagnosis and reconstruction for aeroengine sensor
• Research on fault diagnosis method based on rule base neural network
• Human face recognition based on multidimensional PCA and extreme learning machine
• Optimal Denial-of-Service Attack Scheduling With Energy Constraint

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