オートエンコーダを利用した階層構造物の健全性評価
Author:
Affiliation:
1. Toyohashi University of Technology. Dept. of Mechanical Engineering
2. Waseda University. Dept. of Modern Mechanical Engineering
Publisher
Japan Society of Mechanical Engineers
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/transjsme/89/928/89_23-00227/_pdf
Reference22 articles.
1. Abdeljaber, O., Avci, O. Kiranyaz, S., Gabbouj, M. and Inman, D.J., Real-time vibration-based structural damage detection using one-dimensional convolutional Neural network, Journal of Sound and Vibration, Vol. 388, (2017), p. 154-170, DOI: 10.1016/j.jsv.2016.10.043.
2. Arakawa, T. and Kikunaga, Y., Structural health monitoring and evaluation of damping characteristics for a high rise steel building based on measurement data，AIJ journal of technology and design, Vol. 19, No. 42 (2013), pp. 419-424 (in Japanese).
3. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M. and Inman, D.J., Review: A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications, Mechanical Systems and Signal Processing, Vol. 147, (2021), DOI: 10.1016/j.ymssp.2020.107077.
4. Figueiredo, E., Park, G., Figueiras, J., Farrar, C., and Worden, K., Structural health monitoring algorithm comparisons using standard data sets, Los Alamos National Laboratory Report, LA-14393, (2009).
5. Fujimoto, T., Iwasaki, A., Yamagishi, T., Nakano, K., Nakamura, H., Oikawa, S. and Yamamoto, K., Study of the abnormal vibration detection method for in-service structure using semi-supervised Learning by autoencoder, Transactions of the JSME (in Japanese), Vol. 89, No. 923 (2023), DOI: 10.1299/transjsme.23-00074.
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