Institute of Engineering Mechanics, China Earthquake Administration, Harbin 061019, China.
School of Civil Engineering, Chongqing University, Chongqing 400045, China.
Sensors (Basel). 2021 Sep 19;21(18):6283. doi: 10.3390/s21186283.
Measuring displacement response is essential in the field of structural health monitoring and seismic engineering. Numerical integration of the acceleration signal is a common measurement method of displacement data. However, due to the circumstances of ground tilt, low-frequency noise caused by instruments, hysteresis of the transducer, etc., it would generate a baseline drift phenomenon in acceleration integration, failing to obtain an actual displacement response. The improved traditional baseline correction methods still have some problems, such as high baseline correction error, poor adaptability, and narrow application scope. This paper proposes a deep neural network model based on empirical mode decomposition (EMD-DNN) to solve baseline correction by removing the drifting trend. The feature of multiple time sequences that EMD obtains is extracted via DNN, achieving the real displacement time history of prediction. In order to verify the effectiveness of the proposed method, two natural waves (EL centro wave, Taft wave) and one Artificial wave are selected to test in a shaking table test. Comparing the traditional methods such as the least squares method, EMD, and DNN method, EMD-DNN has the best baseline correction effect in terms of the evaluation indexes: Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and degree of fit (R-Square).
在结构健康监测和地震工程领域，测量位移响应是至关重要的。对加速度信号进行数值积分是一种常见的位移数据测量方法。然而，由于地面倾斜、仪器产生的低频噪声、传感器的滞后等情况，在加速度积分中会产生基线漂移现象，无法获得实际的位移响应。改进的传统基线校正方法仍然存在一些问题，例如基线校正误差高、适应性差、应用范围窄等。本文提出了一种基于经验模态分解（EMD-DNN）的深度神经网络模型，通过去除漂移趋势来解决基线校正问题。通过 DNN 提取 EMD 获得的多个时间序列的特征，实现对真实位移时程的预测。为了验证所提出方法的有效性，选择了两个自然波（EL 中心波、塔夫特波）和一个人工波在振动台上进行测试。通过比较传统的方法，如最小二乘法、EMD 和 DNN 方法，在评价指标方面，EMD-DNN 具有最好的基线校正效果：平均绝对误差（MAE）、均方误差（MSE）、均方根误差（RMSE）和拟合度（R-Square）。
