Detection of multi-type data anomaly for structural health monitoring using pattern recognition neural network

Shun Weng; Ke Gao; Zhi-Dan Chen; Hong Ping Zhu; Li-Ying Wu

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자료유형: 학술저널

저자정보: Shun Weng (Huazhong University of Science and Technology) Ke Gao (Huazhong University of Science and Technology) Zhi-Dan Chen (Huazhong University of Science and Technology) Hong Ping Zhu (Huazhong University of Science and Technology) Li-Ying Wu (Huazhong University of Science and Technology)

저널정보: 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.29 No.1

발행연도: 2022.1

수록면: 129 - 140 (12page)

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The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

#data anomaly detection #feature extraction #pattern recognition neural network #structural health monitoring

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