Study of the abnormal vibration detection method for in-service structure using semi-supervised Learning by autoencoder

Author:

FUJIMOTO Tatsuya1,IWASAKI Atsushi1,YAMAGISHI Takatoshi2,NAKANO Kazuhisa2,NAKAMURA Hiroyuki2,OIKAWA Satoshi3,YAMAMOTO Kouji3

Affiliation:

1. Gunma University

2. Nohmi Bosai LTD

3. Central Nippon Expressway Company Limited

Publisher

Japan Society of Mechanical Engineers

Subject

General Medicine

Reference15 articles.

1. Data J, Figueroa B.E, Lopez D. V, Meruane N. M. .and Ramos M, Capsule Neural Networks for structural damage localization and quantification using transmissibility, Applied Soft Computing Journal, Vol. 97 (2020), pp.1–23.

2. Dohi K, Imoto K, Harada N, Niizumi D, Koizumi Y, Nishida T, Harsh P, Endo T, Yamamoto M. and Kawaguchi Y, Description and discussion on dcase 2022 challenge task 2: unsupervised anomalous sound detection for machine condition monitoring applying domain generalization techniques, In arXiv e-prints (2022) pp.1–5.

3. Dohi K, Nishida T, Harsh P, Tanabe R, Endo T, Yamamoto M, Nikaido Y. and Kawaguchi Y, Mimii dg: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task, In arXiv e-prints (2022), pp.1–5.

4. Guo J, Miki Y, Fujita Y, Kiritoshi K. and Ito K, Anomaly Detection of Seismogram with Autoencoder, the Japanese Society for Artificial Intelligence (2020), 06-2P-03, (in Japanese).

5. Harada N, Niizumi D, Takeuchi D, Ohishi Y, Yasuda M. and Saito S, Toyadmos2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions, Detection and Classification of Acoustic Scenes and Events 2021 (2021), pp.1–5.

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