Smart Structures and Systems

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Data anomaly detection for structural health monitoring using a combination network of GANomaly and CNN

  • Liu, Gaoyang (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Niu, Yanbo (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Zhao, Weijian (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Duan, Yuanfeng (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Shu, Jiangpeng (College of Civil Engineering and Architecture, Zhejiang University)
  • Received : 2021.04.05
  • Accepted : 2021.07.29
  • Published : 2022.01.25
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Abstract

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

Keywords

Acknowledgement

The authors would like to thank the organizations of the International Project Competition for SHM (IPC-SHM 2020) ANCRiSST, Harbin Institute of Technology (China), and University of Illinois at Urbana-Champaign (USA) for their generously providing the invaluable data from actual structures. The authors also would like to thank the chairs of IPC-SHM 2020 Prof. Hui Li, and Prof. Billie F. Spencer Jr for their leadership on the competition. The authors would like to gratefully acknowledge the National Natural Science Foundation of China (52108179), the China Postdoctoral Science Foundation (2021M692835, 2021M702866).

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