Wang Xueqin, Xu Shilin, Zhang Ying, Tu Yun, Peng Mingguo
School of Safety Science and Engineering, Changzhou University, Changzhou 213164, China.
Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.
Sensors (Basel). 2024 Sep 15;24(18):5991. doi: 10.3390/s24185991.
As a critical component in industrial production, pipelines face the risk of failure due to long-term corrosion. In recent years, acoustic emission (AE) technology has demonstrated significant potential in online pipeline monitoring. However, the interference of flow-induced noise seriously hinders the application of acoustic emission technology in pipeline corrosion monitoring. Therefore, a pattern-recognition model for online pipeline AE monitoring signals based on blind source separation (BSS) and a convolutional neural network (CNN) is proposed. First, the singular spectrum analysis (SSA) was employed to transform the original AE signal into multiple observed signals. An independent component analysis (ICA) was then utilized to separate the source signals from the mixed signals. Subsequently, the Hilbert-Huang transform (HHT) was applied to each source signal to obtain a joint time-frequency domain map and to construct and compress it. Finally, the mapping relationship between the pipeline sources and AE signals was established based on the CNN for the precise identification of corrosion signals. The experimental data indicate that when the average amplitude of flow-induced noise signals is within three times that of corrosion signals, the separation of mixed signals is effective, and the overall recognition accuracy of the model exceeds 90%.
作为工业生产中的关键部件，管道面临着因长期腐蚀而失效的风险。近年来，声发射（AE）技术在管道在线监测中显示出巨大潜力。然而，流动噪声的干扰严重阻碍了声发射技术在管道腐蚀监测中的应用。因此，提出了一种基于盲源分离（BSS）和卷积神经网络（CNN）的管道声发射在线监测信号模式识别模型。首先，采用奇异谱分析（SSA）将原始声发射信号转换为多个观测信号。然后利用独立分量分析（ICA）从混合信号中分离出源信号。随后，对每个源信号应用希尔伯特-黄变换（HHT）以获得联合时频域图并进行构建和压缩。最后，基于卷积神经网络建立管道源与声发射信号之间的映射关系，以精确识别腐蚀信号。实验数据表明，当流动噪声信号的平均幅度在腐蚀信号平均幅度的三倍以内时，混合信号的分离是有效的，且该模型的整体识别准确率超过90%。
