Nuclear industry pipeline damage identification through a gated recurrent neural network

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (24) : 48-58.

JIANG Qi1，ZHANG Wang2，QU Wenzhong1，XIAO Li1

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Abstract

Ultrasonic guided wave detection technology has the advantages of high efficiency, low cost and convenient detection, and is widely used in pipeline damage detection. However, the propagation of ultrasonic guided wave in the pipeline is affected by the environment such as temperature and load, which seriously interferes with the extraction and recognition of damage information. Therefore, a machine learning pipeline damage identification method based on particle swarm optimization - bidirectional gated recurrent unit - attention mechanism (PSO-BiGRU-Attention) model is proposed in this paper. The model effectively establishes a mapping between raw ultrasonic guided wave data and the state of the pipeline, thereby enhancing the capability of the feature extraction layer to discern damage characteristics, mitigating environmental interferences, and accurately detecting authentic damage signals. Taking the test bench of circulating water cooling pipeline in nuclear industry as the experimental object, the pipeline damage identification experiment under the condition of temperature and load changes was carried out. Through the experimental analysis, it is verified that the model can effectively realize the pipeline damage identification, and the recognition accuracy is better than other models such as recurrent neural network, long short-term memory, bidirectional gated recurrent unit, etc., which proves the effectiveness and superiority of the proposed method in this paper.

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nuclear industry pipeline / damage identification / particle swarm optimization / bidirectional gated recurrent unit / attention mechanism

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JIANG Qi1, ZHANG Wang2, QU Wenzhong1, XIAO Li1. Nuclear industry pipeline damage identification through a gated recurrent neural network[J]. Journal of Vibration and Shock, 2024, 43(24): 48-58

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