Siphon defect recognition model based on the MSCNN-LSTM and attention mechanism

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (22) : 293-302.

ZHU Xuefeng1， FENG Zao2， MA Jun2， FAN Yugang2

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Abstract

In order to avoid physical damage to buried pipelines caused by resonance, most large buildings above ground are designed with sinking siphon. To solve the multi-source and multi-symptom defect recognition problem caused by siphon’s complex structure, the attention mechanism defect recognition method based on Multi-Scale Convolution Neural Network (MSCNN) and Long Short-term Memory Neural Networks (LSTM) was proposed. The pipeline response acoustic signal was acquired through active acoustic detection method. With the original signal as model input, MSCNN was trained to extract important fine-grained local features. Then, multi-scale local features were fused into a feature vector and input into LSTM to extract coarse-grained contextual features underlying time sequence regularity. By introducing the attention mechanism and assigning different weights to the extracted features, the model paid more attention to the features with the highest category discrimination, and the redundant features were filtered out, thereby improving the defect recognition ability. Finally, siphon’s defect recognition was achieved through Softmax classifier at the output. According to the experimental results, the proposed method has faster convergence speed and high defect recognition accuracy rate 98.44%. Moreover, Grad-CAM class activation visualization method was adopted for the process analysis and demonstration of feature learning and defect classification mechanism of the proposed model.

Key words

Siphon / Defect recognition / MSCNN / LSTM / Attention mechanism

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ZHU Xuefeng1， FENG Zao2， MA Jun2， FAN Yugang2. Siphon defect recognition model based on the MSCNN-LSTM and attention mechanism[J]. Journal of Vibration and Shock, 2023, 42(22): 293-302

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