基于Hilbert-Huang变换的电化学噪声解析及其应用

借助Hilbert-Huang变换 (HHT) 研究了Q345B碳钢在模拟混凝土孔隙液中的电化学噪声信号的时频谱，发现相比离散小波变换 (discretewavelet transform，DWT)，HHT在噪声信号识别过程中具有更高的时频分辨率和稳定性，能够从本质上提高对电化学噪声中耦合的亚稳态点蚀信号的解析精度。针对Q345B碳钢处于钝化态、亚稳态点蚀萌发和稳态点蚀生长等不同阶段的噪声特点，提出了一套基于HHT边界谱的腐蚀状态量化指数与腐蚀特征识别方法。借助于在线电化学噪声监测装置，HHT算法将可用于诊断工业环境的腐蚀形态和腐蚀发展趋势。

Analysis of Electrochemical Noise by Hilbert-Huang Transform and Its Application

Time-frequency transition plays a key role in the pattern identification of electrochemical noise (EN) signal. In this paper, we studied the EN of carbon steel in simulated concrete pore solution by Hilbert-Huang transform (HHT), and found that, in comparison with discrete wavelet transform (DWT), HHT exhibited much higher resolution and stability at time-frequency domain for the identification of EN transients. Moreover, HHT can improve the resolving accuracy of metastable pit signals coupled in EN. According to the EN characteristics of carbon steel at different corrosion status, such as passive, metastable, and stably growing pits, we proposed a pitting factor (PF) as an index for identification and quantification of localized corrosion type based on HHT algorithm, aiming at the diagnosis of corrosion type and severity of industrial installation by online EN monitoring instrument.