Noise Identification in Acoustic Emission (AE) Inspection of Oil Tank Bottom Corrosion Based on Multi-Domain Features and BES-SVM Algorithm

作者：Canwei Huang, Wenpei Zhang, Bo Yang, R Q Zheng, Xueliang Sun, Fuhai Chen, Da Xu, Weidong Li · 发表于：Processes · 年份：2025 · DOI：10.3390/pr13103291 · 被引用次数：1 · 研究领域：Ultrasonics and Acoustic Wave Propagation、Non-Destructive Testing Techniques、Structural Integrity and Reliability Analysis

Acoustic emission (AE) is a passive non-destructive testing (NDT) method that allows for online monitoring of oil tank bottom corrosion without production shutdown. However, AE signals are susceptible to ambient noise interference, causing the AE inspection system to mistakenly identify noise as corrosion signals, which significantly reduces AE inspection performance. Therefore, it is important to distinguish between AE signals caused by corrosion and those caused by noise. To address this, an AE inspection platform for vertical atmospheric tank corrosion is established. Six common noise sources in field AE inspections, including mechanical vibration and friction, fluid and raining disturbance, external impacts, and oil leakage are simulated. The impacts of these noises on AE location events are analyzed. Variational mode decomposition (VMD) and dispersion entropy (DE) are used to extract multi-domain features of AE signals. An improved distance evaluation (IDE) algorithm is then introduced to obtain a highly correlated feature subset. A support vector machine (SVM) model optimized by the bald eagle search (BES) algorithm is proposed to identify different noise sources. Field experiments demonstrate that for mechanical friction, external impacts, and effective corrosion signals, the proposed method achieves identification accuracy of 92.95% and 94.00% in the training and test sets, respectively. This proves the reliability of the BES-SVM model, which uses multi-domain feature...