Machine learning and wavelet analysis for diagnosis & classification of faults in belt drive-Reference-Cited by-同舟云学术

Machine learning and wavelet analysis for diagnosis & classification of faults in belt drive

Published:2023 Issue: Volume: Page:
ISSN:0094-243X
Container-title:INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING FOR EMERGING TECHNOLOGIES (ICOMEET 2021)
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Author:

Kumar Sujesh,Lokesha M.,Kiran Kumar M. V.,Istijono Bambang

Publisher

AIP Publishing

Link

http://aip.scitation.org/doi/pdf/10.1063/5.0127650

Reference10 articles.

1. J. Wandel “Vibration based quality control” KTH Engineering Sciences, Stockholm, Licentiate Thesis, TRITA-AVE:27 (2006) Multistage gearboxes.

2. Gianluca Nicchiotti, Luc Fromaigeat, and Lianel Etienne “Machine Learning Strategy for Fault Classification Using Only Nominal Data” European conference of the prognostics and health management society (2016).

3. New applications of the real cepstrum to gear signals, including definition of a robust fault indicator

4. TIME–FREQUENCY ANALYSIS IN GEARBOX FAULT DETECTION USING THE WIGNER–VILLE DISTRIBUTION AND PATTERN RECOGNITION

5. K. P. Ramachandran, Khalid Fathi and B.K.N. Rao “Recent trends in systems performance monitoring & failure diagnosis” The IEEE International conference on industrial engineering & engineering management: 2193–2200, 2010

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1. Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features;Heliyon;2024-07

2. Signal Feature Extract Based on Dual-Channel Wavelet Convolutional Network Mixed With Hypergraph Convolutional Network for Fault Diagnosis;IEEE Sensors Journal;2023-11-15