Unlocking Corrosion Secrets: A Comprehensive Analysis of Acoustic Emission Data Using Continuous Wavelet Transform and Deep Learning for Steel Pipeline Monitoring

Abstract

Abstract The analysis of acoustic emission data in the time and frequency domains can provide useful insights about the state of steel pipelines, although research in this field is limited. The research shortage has been triggered due to specific problems provided by elements such as irregular structure geometries, material dependencies, and insufficient training data. To address the lack of training data, we created a new dataset of scalogram images for deep learning-based classification approaches. Monitoring a 500-meter-long steel oil and gas pipeline provided experimental data. The acoustic emission waveforms were preprocessed, tagged, and classified based on three critical characteristics: AE-mean, kurtosis, and amplitude. The continuous wavelet transform was used to translate these waveform instances into the time-frequency domain. To. The abstract should be an objective representation of the article and it must not contain results that are not presented and substantiated in the main text and should not exaggerate the main conclusions. State-of-the-art deep convolutional neural networks were used as benchmarks to assess the effectiveness of the proposed dataset. Surprisingly, the unique dataset achieved a classification accuracy of 91.0%. Furthermore, the impact of initial learning rate and L2 regularization hyperparameters were evaluated for EfficientNet-b0. The availability of this dataset opens the door to a plethora of corrosion detection applications, providing substantial prospects in this field.