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Data Anomaly Detection for Bridge SHM Based on CNN Combined with Statistic Features

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Abstract

Structural health monitoring of long-span bridge has received increasing attention in recent years. In order to achieve accurate monitoring, the integrity of data collection should be guaranteed. Unfortunately, these data inevitably contain a variety of types of anomalies due to sensor faults, harsh environments, and other issues. Identifying anomalies from the data is essential to ensure credible monitoring results. Machine learning methods have the potential to detect data anomaly automatically. However, a well-performed convolutional neural network (CNN) model requires a large number of balanced training samples and frequent tuning. When similar anomalous patterns exist in complex systems, the tuning process might be time-consuming. In this paper, a data anomaly detection method is proposed based on CNN combined with statistic features. Firstly, acceleration data are downsampled, stacked, and input into CNN as the training set. A CNN model is designed and trained. Intermediate results are obtained through the model. Subsequently, the statistic features are applied to analyze and classify the confusable patterns. The novelty of this framework is that it combines the advantages of CNN and statistical features, which can realize data anomaly detection faster and more accurately than using CNN alone. The results of the acceleration data from a bridge demonstrate the effectiveness of the proposed approach to identify the anomalous data.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (Grant Nos. 52175073, 51805015, 91860205), the Open Foundation of Henan Key Laboratory of Underwater Intelligent Equipment (Grant No. KL03B2105), and the Open Foundation of State Key Laboratory of Digital Manufacturing Equipment and Technology (Grant No. DMETKF2021006), which are highly appreciated by the authors. The authors would like to thank the organizations of the International Project Competition for SHM (Grant No. 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.

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Authors and Affiliations

  1. School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China

    Han Zhang & Tong Tong

  2. Science & Technology on Reliability and Environmental Engineering Laboratory, Beihang University, Xueyuan Road No. 37, Haidian District, Beijing, China

    Jing Lin, Jiadong Hua & Fei Gao

  3. Advanced Manufacturing Center, Ningbo Institute of Technology, Beihang University, Ningbo, 315100, China

    Jiadong Hua & Fei Gao

  4. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Jiadong Hua

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  1. Han Zhang
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Correspondence to Jiadong Hua.

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Zhang, H., Lin, J., Hua, J. et al. Data Anomaly Detection for Bridge SHM Based on CNN Combined with Statistic Features. J Nondestruct Eval 41, 28 (2022). https://doi.org/10.1007/s10921-022-00857-2

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  • DOI: https://doi.org/10.1007/s10921-022-00857-2

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