Key Laboratory for Metallurgical Equipment and Control of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2023 Jan 11;23(2):855. doi: 10.3390/s23020855.
In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.
在这项研究中，研究了基于压电主动传感的时反法来监测管道内部腐蚀。提出了一种将小波包能量与卷积神经网络（CNN）相结合的有效方法，用于识别管道的内部腐蚀状态。将两个锆钛酸铅（PZT）贴片粘贴在管道的外表面上，作为产生和接收超声波信号的传感器，这些超声波信号在管道内壁传播。然后，采用时反技术对接收的响应信号在时域中进行反转，然后将其重新发送作为激励信号，以获得聚焦信号。之后，采用小波包变换对聚焦信号进行分解，提取具有较大分量的小波包能量（WPE）作为 CNN 模型的输入，以快速识别管道内部的腐蚀程度。进行了腐蚀实验以验证所提出方法的正确性。通过电化学腐蚀产生和发展管道的腐蚀，在样品管道上施加了九个不同深度的腐蚀。实验结果表明，分类准确率超过 99.01%。因此，该方法可以定量监测管道的腐蚀状态，并能及时准确地确定管道的内部腐蚀程度。结合所提出的时反法的 WPE-CNN 模型具有监测管道内部腐蚀的高应用潜力。
