Hierarchical structural health monitoring for health management of mechanical assets by digital twin-Reference-Cited by-同舟云学术

Hierarchical structural health monitoring for health management of mechanical assets by digital twin

Published:2022 Issue:910 Volume:88 Page:22-00095-22-00095
ISSN:2187-9761
Container-title:Transactions of the JSME (in Japanese)
language:en
Short-container-title:

Author:

TAKEDA Norio¹,KAMEYAMA Tatsuya²

Affiliation:

1. Center for Sustainability -Production Engineering and MONOZUKURI, R&D Group, Hitachi, Ltd.

2. Renewable Energy Solutions Division, Energy Business Unit, Hitachi, Ltd.

Publisher

Japan Society of Mechanical Engineers

Link

https://www.jstage.jst.go.jp/article/transjsme/88/910/88_22-00095/_pdf

Reference14 articles.

1. Alemayehu, F. M. and Ekwaro-Osire, S., Probabilistic performance of helical compound planetary system in wind turbine, Journal of Computational and Nonlinear Dynamics, Vol. 10, No. 4 (2015), pp. 041003-12.

2. Asgarpour, M. and Sorensen, J., Bayesian based prognostic model for predictive maintenance of offshore wind farms, International Journal of Prognostics and Health Management, Vol. 9, No.1, 10 (2018), pp. 1-9.

3. Endo, T., Matsuishi, M., Mitsunaga, K., Kobayashi, K. and Takahashi, K., Rain flow method, the proposal and the applications, Memoirs of Kyusyu Institute of Technology, No. 28 (1974), pp. 33-62 (in Japanese).

4. Ewing, F., Thies, P. R., Shek, J. K., Bittencourt, C. and Tian, Z., A physics-based prognostics approach for tidal turbines, 2019 IEEE International Conference on Prognostics and Health Management (ICPHM) (2019), June 17-19, CA, USA.

5. Hasegawa, T., Ogata, J., Murakawa, M., Kobayashi, T. and Ogawa, T., Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring, International Journal of Prognostics and Health Management, Vol. 8, No. 2, 20 (2017), pp. 1-8.