Detection of the pipeline elbow erosion by percussion and deep learning

Abstract

Elbows are commonly used in pipelines to change the direction of flow, and the pipeline elbows are prone to erosion caused by the transported medium. Detection of the pipeline elbow erosion is critical to the health of the pipeline system. Currently, most of the detection methods of the pipeline elbow erosion level require the installation of the constant-contact sensor, which are constrained under certain environments. This paper proposes a novel detection method for the pipeline elbow erosion, which is easy-to-implement, low-cost, and free of the installation of a constant-contact-sensor. The proposed method combines percussion, variational mode decomposition (VMD), and deep learning. The percussion-induced audio signal (single-hit) is decomposed into seven modes using VMD, and the multiple random convolutional kernel transform (Multi-Rocket), a machine learning method, is used to select the most representative mode for the single-hit audio signal. Finally, the capsule neural networks, a deep learning method, is utilized to classify the most representative mode under six levels of erosion severity (six classes). To verify the effectiveness of the proposed method, two case studies are conducted under three pipeline elbows with similar structure and dimension. In case study I, all the methods achieve similar classification accuracy (around 100%) under six erosion levels. In case study II, the proposed method (>90%) is far more effective than other shallow learning and deep learning methods (all < 80%) in terms of the classification accuracy. The results demonstrate the superiority of the proposed method in the detection of pipeline elbow erosion levels. To the best of our knowledge, this paper is the first attempt to study the detection of pipeline elbow erosion levels by combining the percussion method and deep learning.