A Feature Selection Committee Method Using Empirical Mode Decomposition for Multiple Fault Classification in a Wind Turbine Gearbox
Author:
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials
Link
https://link.springer.com/content/pdf/10.1007/s10921-023-00996-0.pdf
Reference29 articles.
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2. Sánchez, R.V., Lucero, P., Vásques, R.E., Cerrada, M., Macancela, J.C., Cabrera, D.: Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN. J. Intell. Fuzzy Syst. (2018). https://doi.org/10.3233/JIFS-169526
3. Desavale, R.G., Jadhav, P.M., Dharwadkar, N.V.: Dynamic response analysis of gearbox to improve fault detection using empirical mode decomposition and artificial neural network techniques. J. Risk Uncertain. Eng. Syst (2021). https://doi.org/10.1115/1.4051344
4. Lei, Y., Zuo, M.J.: Gear crack level identification based on weighted k nearest neighbor classification algorithm. Mech. Syst. Signal Process. (2009). https://doi.org/10.1016/j.ymssp.2009.01.009
5. Praveenkumar, T., Sabhrish, B., Saimurugan, M., Ramachandran, K.I.: Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox. Measurement (2018). https://doi.org/10.1016/j.measurement.2017.09.041
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