.png){kind=icon}
.png){kind=icon}
Return
Pattern Recognition of Crack Extension in Oil and Gas Pipelines Based on Acoustic Emission Signals
.png){kind=icon}
PRE
AI
J
Z
DOI：10.1007/s10921-026-01409-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To address the insufficient real-time capability and strong noise interference associated with conventional crack detection methods for oil and gas pipelines, this paper proposes a crack propagation pattern recognition method that integrates acoustic emission (AE) signals with intelligent algorithms. An improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is introduced to denoise AE signals. Wavelet packet energy spectrum analysis and kernel principal component analysis (KPCA) are then combined to extract the principal components of frequency-band energy, and multi-channel data are fused by a feature serial fusion strategy to construct comprehensive feature vectors. Furthermore, a convolutional neural network optimized by the sparrow search algorithm (SSA-CNN) is developed to accurately classify three stages of crack propagation: crack initiation, stable propagation, and unstable propagation. The experimental results show that the proposed method can effectively identify crack propagation patterns under both sufficient-sample and small-sample conditions. Compared with the conventional SVM and unoptimized CNN models, the recognition accuracy is significantly improved, reaching a maximum of 98.67%, which verifies the robustness and engineering applicability of the proposed method. This study provides a new technical approach for real-time safety monitoring of oil and gas pipelines and is of great significance for improving pipeline operation and maintenance efficiency as well as accident early-warning capability.
Keywords:
Acoustic emission technology
Crack propagation pattern recognition
CEEMDAN algorithm
Wavelet packet energy spectrum
Kernel principal component analysis
SSA-CNN model
Journal
IF:
2.4
Papers:
265
Citations:
2.7K
Organization
Cited Papers