Summary
Data reconstruction is usually used for lost data recovery and data supplement in structural health monitoring (SHM) system. In this paper, a data reconstruction algorithm is proposed for the entire time series data missing at dispersed and consecutive multiple observation points. Kriging‐based sequence interpolation (KSI) is adopted for primary data reconstruction and probability density function (PDF) reconstruction. In terms of dispersed data missing, the reconstructed PDF is of great accuracy, which is used to calculate the exact standard deviation to correct the primary reconstructed data and obtain satisfactory performance. Regarding the consecutive data missing, the first correction is insufficient due to the missing of too much information. In addition, it is found to exist a fuzzy mapping relation between the exact standard deviation and extreme values of wind speed series. Thus, the quantile regression combined with deep neural network is proposed for probability reconstruction of the extreme values. The point estimations are adopted to the secondary correction for the reconstructed data with first correction. The effectiveness and validity of the secondary correction strategy have been testified by structural dynamic response analysis. The extreme value of the displacement response under the reconstructed data with the secondary correction is closer to that under the actual wind data, compared with the reconstructed data with first correction.
Wind signal forecasting has become more and more crucial in the structural health monitoring system and wind engineering recently. It is a challenging subject owing to the complicated volatility of wind signals. The robustness and generalization of a predictor are significant as well as of high precision. In this paper, an adaptive residual convolutional neural network (CNN) is developed, aiming at achieving not only high precision but also high adaptivity for various wind signals with varying complexity. Afterwards, reinforced forecasting is adopted to enhance the robustness of the preliminary forecasting. The preliminary forecast results by adaptive residual CNN are integrated with historical observed signals as the new input to reconstruct a new forecasting mapping. Meanwhile, simplified-boost strategy is applied for more generalized results. The results of multistep forecasting for five kinds of nonstationary non-Gaussian wind signals prove the more excellent adaptivity and robustness of the developed two-stage model compared with single models.
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