@inproceedings{ba469923b2784303ba140449e3bd9777,
title = "Anomaly Detection with Universal Representation of Modal Testing Response Data",
abstract = "In structural testing, modal analysis of the response data reflects the specifications of a structure. This paper proposes a data-driven anomaly detection method using the universal representation of modal testing response data. High-frequency time series data are preprocessed and transformed into truncated frequency response signals. Subsequently, the universal representation learning of frequency response signals is realized through contrastive learning based on TS2Vec, and distance-based anomaly detection is performed using the K-nearest neighbor algorithm with semi-supervised learning. For a limited excitation experimental dataset consisting of 32 samples, the proposed method achieves a detection rate of 80.0\%. This result demonstrates the validity of the universal representation of modal testing response data.",
keywords = "anomaly detection, frequency response, modal test, rotating machinery, signal",
author = "Chao Jiang and Haoyu Wang and Xuan Han and Liang Yu and Yingjun Deng",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023 ; Conference date: 26-08-2023 Through 29-08-2023",
year = "2023",
doi = "10.1109/ICRMS59672.2023.00030",
language = "英语",
series = "Proceedings - 2023 14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "105--110",
editor = "Liming Ren and Wong, \{W. Eric\} and Hailong Cheng and Xiaopeng Li and Shu Wang and Kanglun Liu and Ruifeng Li",
booktitle = "Proceedings - 2023 14th International Conference on Reliability, Maintainability and Safety, ICRMS 2023",
}