Supervised Machine Learning Based Approach for Early Fault Detection in Polymer Gears Using Vibration Signals
Author:
Publisher
Springer Science and Business Media LLC
Subject
Physics and Astronomy (miscellaneous)
Link
https://link.springer.com/content/pdf/10.1007/s12647-022-00608-8.pdf
Reference44 articles.
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2. G. Yu, H. Liu, K. Mao, C. Zhu and Z. Lu, Examination on the wear process of polyformaldehyde gears under dry and lubricated conditions. Friction, 9 (2021) 538–550. https://doi.org/10.1007/s40544-020-0362-7.
3. Z. Lu, H. Liu, P. Wei, C. Zhu, D. Xin and Y. Shen, The effect of injection molding lunker defect on the durability performance of polymer gears. Int J Mech Sci, 180 (2020) 105665. https://doi.org/10.1016/j.ijmecsci.2020.105665.
4. R.F. Handschuh, K.E. Laberge, S. Deluca and R. Pelagalli, Vibration and operational characteristics of a composite-steel (hybrid) gear 13 (2014) 80–87.
5. E. Sim, C. Kim, K.S. Kwak and B. Kim, Optimum interface shape and vibration test for a new transmission helical gear composed of steel and aramid/phenol composite. J Mech Sci Technol, 34 (2020) 1629–1634. https://doi.org/10.1007/s12206-020-0325-y.
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