Classification method for frictional vibration signals of floating ring seal based on QSE-ResNet

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Journal of Vibration and Shock ›› 2024, Vol. 43 ›› Issue (21) : 194-201.

LIU Wei, ZHANG Shuyao, ZHAI Zhixing, ZHU Shuhai, LI Shuangxi

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Abstract

In order to solve the problem that the vibration signal characteristics of the floating ring sealing device are weak and difficult to identify under different friction conditions, the traditional deep learning network has achieved remarkable results in performance. However, due to the problems of many hyperparameters, long training time, many iterations, low network accuracy and high computational cost, the model has certain limitations in practical use. Therefore, this study proposes a new method based on the Deep Residual Network (ResNet), which is based on the Fast Attention Mechanism (Quick Squeeze Excitation ResNet), to solve the problem in the classification of friction and vibration signals of floating ring seals. This method improves the performance of the model by introducing an attention mechanism, adjusting the connection of the network residual blocks, and selecting a specific optimizer, and compares the test with the other four models on the same Mel spectrogram dataset. The results show that the accuracy of QSE-ResNet reaches 97%, which is 13% higher than that of traditional convolutional neural networks (CNN), while shortening the number of model iterations by 55% and saving 30% of network training time. In addition, QSE-ResNet successfully solves the problems of overfitting, gradient explosion and gradient vanishing, which significantly shortens the number of iterations, saves network training time and improves test accuracy, making the signal status monitoring and equipment deployment of floating ring seal friction vibration more convenient. The QSE-ResNet proposed in this study makes the floating ring seal friction and vibration model more convenient to deploy, and provides a new idea for the study of floating ring seal friction vibration signal.

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floating ring sealing / friction and vibration / acoustic emission / deep learning / feature classification

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LIU Wei, ZHANG Shuyao, ZHAI Zhixing, ZHU Shuhai, LI Shuangxi. Classification method for frictional vibration signals of floating ring seal based on QSE-ResNet[J]. Journal of Vibration and Shock, 2024, 43(21): 194-201

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