A Method for the Pattern Recognition of Acoustic Emission Signals Using Blind Source Separation and a CNN for Online Corrosion Monitoring in Pipelines with Interference from Flow-Induced Noise

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 5991
• Main Authors: Wang, Xueqin, Xu, Shilin, Zhang, Ying, Tu, Yun, Peng, Mingguo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.09.2024; MDPI
• Subjects: acoustic emission; Acoustic emission testing; Acoustics; Algorithms; blind source separation; convolutional neural network; Corrosion; Eigenvalues; Eigenvectors; Industrial production; Methods; Neural networks; Nondestructive testing; online monitoring; Pattern recognition; pipeline corrosion; Steel; Technology application; acoustic emission; blind source separation; pipeline corrosion; convolutional neural network; online monitoring
• ISSN: 1424-8220; 1424-8220
• 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%.