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| Self-adaptive Denoising of Carbon Steel Corrosion Monitoring Signal Based on EMD and Wavelet |
| Received:April 18, 2018 Revised:July 25, 2018 |
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| DOI：10.7643/ issn.1672-9242.2018.07.010 |
| KeyWord:resistance probe carbon steel corrosion empirical model decomposition wavelet threshold denoising maximum information coefficient |
| Author | Institution |
| ZHANG Hui-jie |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
| FU Dong-mei |
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing , China |
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| Abstract: |
| Objective To recover effective corrosion information (long-term trends and periodic narrow-band spikes) from monitoring signal, and propose an adaptive denoising algorithm (EMD-WTD) based on Empirical Mode Decomposition (EMD) and Wavelet Threshold Denoised (WTD). Methods First, the signal was decomposed by EMD. The maximum information coefficient (MIC) was used to judge the demarcation point of the noise-dominated and effective-dominated signal components. Then the noise-dominated signal was subjected to adaptive wavelet threshold denoising. Finally, Analog signal and corrosion monitoring signal were used to validate the proposed algorithm. Results EMD-WTD algorithm could effectively remove noise with improving signal to noise ratio by more than 10dB. Conclusion EMD-WTD algorithm could better preserve the periodic narrow-band spike information than multi-denoising algorithm. Moreover, EMD-WTD algorithm lays the foundation for establishment of mathematical models of between corrosion signals and environment. |
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