Application of genetic algorithms for parameters identification in a developing smart gear system

Author:

Mac T. T.ORCID,Iba D.,Matsushita Y.,Mukai S.,Inoue T.,Fukushima A.,Miura N.,Iizuka T.,Masuda A.,Moriwaki I.

Funder

Grant-in-Aid for Scientific Research (A)

Publisher

Springer Science and Business Media LLC

Subject

General Engineering

Reference23 articles.

1. Demsey PJ, Lewicki DG, Le DD (2007) Investigation of current method to identify helicopter gear health. In: Aerospace conference 2007 IEEE, pp 1–13

2. Nowoisky IS, Grzeszkowski M, Mokhtari N, Pelham JG, Gühmann C (2019) Monitoring concept study for aerospace power gear box drive train. In: VDI 3rd International Conference on Gears, pp 269–286

3. Kien BH, Iba D, Ishii Y, Tsutsui Y, Miura N, Iizuka T, Ma-suda A, Sone A, Moriwaki I (2019) Crack detection of plastic gears using a convolutional neural network pre-learned from im-ages of meshing vibration data with transfer learning. Forsch Ingenieurwes 83:645–653

4. König F, Marheineke J, Jacobs G, Sous C, Zuo MJ, Tian Z (2021) Data-driven wear monitoring for sliding bearings using acoustic emission signals and long short-term memory neural networks. Wear 476:203616

5. Pullin R, Clarke A, Eaton MJ, Holford KM, Evans SL, McCory JP (2012) Identification of the onset of cracking in gear teeth using acoustic emission. J Phys: Conf Ser 382:1

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