Multiclass Fault Classification of an Induction Motor Bearing Vibration Data Using Wavelet Packet Transform Features and Artificial Intelligence
Author:
Publisher
Springer Science and Business Media LLC
Subject
Microbiology (medical),Immunology,Immunology and Allergy
Link
https://link.springer.com/content/pdf/10.1007/s42417-022-00733-3.pdf
Reference29 articles.
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3. Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16(7–8):657–665. https://doi.org/10.1016/j.engappai.2003.09.006
4. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11(2):2300–2312. https://doi.org/10.1016/j.asoc.2010.08.011
5. Vakharia V, Gupta VK, Kankar PK (2015) A multiscale permutation entropy based approach to select wavelet for fault diagnosis of ball bearings. JVC/J Vib Control 21(16):3123–3131. https://doi.org/10.1177/1077546314520830
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1. Bearing Fault Diagnosis Method Based on Multi-Domain Feature Selection and the Fuzzy Broad Learning System;Processes;2024-02-10
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