# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 3 Experimental Design and Evaluation Criteria  
### Data Description  
The data used in this study were obtained from a vibration isolation test on an LNG storage tank carried out in the shaking-table laboratory of Chongqing University, and the  
Figure 3: Structure of the EEMD-LSTM.  
test model is shown in Figure 4. The effects of different levels of earthquakes on the tank's structure were simulated. In order to measure the acceleration response of the structure under each test condition, MEAS accelerometers and ENDEVCO accelerometers were used.  
Pile: One acceleration sensor was arranged in the \(x\)-, \(y\)-, and \(z\)-directions at the pile head of the tank;  
Inner tank: There were four measurement points along the height direction in the inner tank without vibration isolation, and one acceleration sensor was arranged in the \(x\)-, \(y\)-, and \(z\)-directions for each measurement point;  
Outer tank: there were seven measurement points in the tank and its dome (two measurement points in the dome and five measurement points in the tank along the height direction), and one acceleration sensor was arranged the \(x\)-, \(y\)-, and \(z\)-directions for each measurement point. The layout of the acceleration sensors is shown in Figure 5.  
Using the original acceleration data of the tank in the Chongqing University shaking-table lab, the performance of the EEMD-LSTM method proposed was compared with some traditional deep learning prediction methods. The sampling frequency of the dataset's acceleration sensor was 256 Hz, collecting raw data every 0.0039 s. All experiments in this study only compared the performance of the various prediction methods. There was no need to use the complete data of all working conditions. Therefore, the data can reflect the structural power characteristics to evaluate the proposed method's performance.  
In this paper, the acceleration data measured by the accelerometers under white noise (White), El-Centro wave (EL), and artificial wave (RG) excitation of the storage tank's structure were used as the dataset for the proposed model. In order to briefly describe the essential characteristics of the acceleration response data, five types of statistical indicators of the dataset are shown, including the total sample size, mean, standard deviation, and minimum and maximum values, in Table 1.  
Based on the original data collected for reconstruction, each dataset was divided into two parts using 80% of the dataset as the training set and the remaining 20% as the test set. The training dataset contained 12,288 sampling points, and the test dataset contained the remaining 3072 sample points. As shown in Figure 6, the solid boxes' data were the input data for predicting the values in the next step. The dashed boxes indicate the missing data during the sensor measurement of the signal data. For the n missing data points, each input and output pair consisted of 256 + n samples, including 256 input samples (e.g., 1-256) and n output samples (e.g., 257-(257 + n)), i.e., missing data. Then, the input-output pairs were fed into the EEMD-LSTM model for training to obtain a model capable of predicting n missing signal data points. The model parameters were updated at each time step during  
Figure 4: The experimental model of liquid natural gas [21].  
the training process while remaining constant during the testing process. Section 4 of this paper show the results of using the EEMD-LSTM model to predict missing signal data of different lengths (1, 5, 10, and 15 missing data).