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Baseline Correction of Acceleration Data Based on a Hybrid EMD-DNN Method.

Zengshun Chen1,2, Jun Fu2, Yanjian Peng3

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Summary

A new EMD-DNN model effectively corrects baseline drift in acceleration data for accurate structural displacement monitoring. This method outperforms traditional techniques, improving seismic engineering and structural health monitoring accuracy.

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baseline correctionbaseline driftdeep neural networkdisplacement measurementempirical mode decomposition

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Area of Science:

  • Structural Health Monitoring
  • Seismic Engineering
  • Signal Processing

Background:

  • Accurate displacement response measurement is crucial for structural health monitoring and seismic engineering.
  • Numerical integration of acceleration signals is a common method, but baseline drift is a significant challenge.
  • Existing baseline correction methods often suffer from high errors, poor adaptability, and limited application scope.

Purpose of the Study:

  • To propose a novel deep neural network model, Empirical Mode Decomposition-Deep Neural Network (EMD-DNN), for effective baseline drift correction in acceleration data.
  • To accurately predict the real displacement time history by removing drifting trends.
  • To evaluate the performance of the EMD-DNN model against traditional methods.

Main Methods:

  • Empirical Mode Decomposition (EMD) was used to decompose the acceleration signal into multiple intrinsic mode functions.
  • A Deep Neural Network (DNN) was employed to extract features from the multiple time sequences obtained by EMD.
  • The proposed EMD-DNN model was validated using shaking table tests with natural (EL Centro, Taft) and artificial seismic waves.

Main Results:

  • The EMD-DNN model demonstrated superior baseline correction performance compared to the least squares method, EMD alone, and DNN alone.
  • Quantitative evaluation using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-Square showed significant improvements.
  • The model successfully predicted the real displacement time history, effectively removing baseline drift.

Conclusions:

  • The proposed EMD-DNN model offers a robust and adaptable solution for baseline drift correction in displacement response measurements.
  • This advanced method significantly enhances the accuracy of structural health monitoring and seismic data analysis.
  • The findings suggest EMD-DNN as a promising approach for real-world applications requiring precise displacement estimation.