A combined method of autoregressive model and matrix factorization for recovery and forecasting of cyclic structural health monitoring data

Wang, Chunsheng; Zhang, Peijie

doi:10.1016/j.ymssp.2023.110703

Cited by 4 publications

(1 citation statement)

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“…Wang C and Zhang P presented a combined method of autoregressive model and matrix factorization method to reach the tasks of data imputation and prediction. The accuracy of this method was verified using on-site temperature monitoring data of the high-speed railway bridge [21].…”

Section: Introductionmentioning

confidence: 93%

A data recovery method for extra-long-span railway bridge health monitoring based on TVFEMD and CNN-GRU

Hao,

Liu,

et al. 2024

Meas. Sci. Technol.

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Structure health monitoring (SHM) systems play a crucial role in understanding the condition of structures. However, owing to various uncertain factors, sensor data may be missing, which poses a great challenge to capture the real-time dynamic characteristics of the bridge. To address this problem, this paper proposes a model that integrates time varying filtering based empirical mode decomposition (TVFEMD), convolutional neural network (CNN), and gated recurrent unit (GRU) to forecast and fill in the missing data. The proposed model initially employs TVFEMD to decompose the signal into several intrinsic mode functions (IMFs) of different frequency bands. Subsequently, CNN is utilized to extract data features for each IMF, followed by prediction through GRU. The model linearly combines the prediction results obtained from each IMF to obtain the actual prediction result. Simulation data and measured data from the large railway bridge are utilized in this research to validate the model’s efficacy. The analysis results demonstrate a significant improvement in prediction performance compared to traditional models, showcasing strong generalization ability and robustness. In conclusion, the model proposed in this paper has a high utilization value in health monitoring data recovery.

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Section: Introductionmentioning

confidence: 93%