# A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring
###### Abstract  
Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of the method is to predict missing data using the raw data and decomposed subsequence as the decomposed subsequence can improve the predicted accuracy. In addition, the BiGRU in the hybrid model can extract the pre-post correlations of subsequence compared with traditional artificial neural networks. Raw acceleration data collected from a three-story structure are used to evaluate the performance of the EMD-BiGRU for missing data imputation. The recovery results of measure data show that the EMD-BiGRU exhibits excellent performance from two perspectives. First, the decomposed subsequence can improve the accuracy of the BiGRU predicted model. Second, the BiGRU outperforms other machine learning algorithms because it captures more microscopic changes of measured data. The experimental analysis suggests that the change patterns of raw measured signal data are complex, and therefore it is significant to extract the features before modeling. structural health monitoring; deep learning; data loss recovery; empirical mode decomposition; bidirectional gated recurrent unit +
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have studied constructed technology to recover missing data to support the application of SHM systems. Large amounts of missing and corrupted monitoring data can reduce the accuracy of algorithms or model analysis, which can introduce large deviations in certain statistical inferences or decision-making. To reduce the risk of making incorrect inferences and improve structural health assessment, it is essential to find suitable methods for handling missing data in SHM. In recent years, many researchers have presented many methods to address the problem. One type of method attempts to reconstruct the complete signal from the raw sensory data. Such as, [PERSON] [4] proposed a method of missing data repair using fast-moving wireless sensing technology and investigated its mechanism through experimental analysis. It indicated that the doppler effect was the main factor leading to missing data. Based on this, [PERSON] adopted compressed sampling technology to restore the missing data and verified its effectiveness with acceleration monitoring data from the Songpu Bridge in Harbin, China. [PERSON] [5] proposed a Bayesian compressive sampling algorithm that can be used to reconstruct near-sparse signals and applied to repair lost acceleration signals in health monitoring. He noted that this algorithm was equally effective for the signals with low sparsity levels. [PERSON] [6] used two finite impulse response filters to reconstruct the displacement and velocity from the acceleration. Then this method was evaluated by using wind tunnel data, and the experimental result showed that the proposed method had high accuracy. Meanwhile, considering that randomly missing data is equivalent to compressed data in compressive sensing, [PERSON] [7] studied the application of compressive sensing theory to recover missing data from wireless sensors of structural health monitoring. He adopted random demodulation techniques to embed the compression-aware algorithm into wireless sensors. The field test results on the Songpu Bridge in Harbin showed that missing data reconstruction based on the compression-aware theoretical algorithm was feasible. [PERSON] [8] embedded a missing data repair algorithm based on compressed samples into the Imote2 wireless accelerometer to handle missing data problems during signal transmission.