Study of dry gas seal friction lubrication state identification with improved DenseNet

ZHANG Shuai1, DING Xuexing1, WANG Shipeng2, LI Ning3, ZHANG Lanxia1

Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (4) : 313-321.

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Journal of Vibration and Shock ›› 2025, Vol. 44 ›› Issue (4) : 313-321.
FAULT DIAGNOSIS ANALYSIS

Study of dry gas seal friction lubrication state identification with improved DenseNet

  • ZHANG Shuai1, DING Xuexing*1, WANG Shipeng2, LI Ning3, ZHANG Lanxia1
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Abstract

In order to address the difficulties in measuring key contact state parameters (such as membrane thickness and open time of end faces) during the operation of dry gas seals, a method for recognizing the friction lubrication states of dry gas seal end faces is proposed by integrating a self-attention mechanism with a DenseNet-CBAM (Convolutional Block Attention Module) network. Based on the Stribeck curve and the operating laws of dry gas seals, the potential friction lubrication states of the end faces are identified as fluid lubrication, boundary lubrication, and mixed lubrication. Acoustic emission signals generated during the operation of the seal system are collected using sensors, and characteristic components representing various friction lubrication states are extracted through filtering, time-domain analysis, and frequency-domain analysis. A 3D Continuous Wavelet Transform (3D-CWT) is applied to generate time-frequency maps, and ultimately, the deep learning model DenseNet-CBAM is employed to recognize these maps and identify the friction lubrication states of the seal system. The 3D-CWT time-frequency map improves the accuracy of state identification compared to other 2D time-frequency feature maps as inputs.Compared to other deep learning models, this approach achieves a high precision of 99.27% in identifying the friction lubrication states of dry gas seals.

Key words

dry gas seal / dense connection network / self-attention mechanism / acoustic emission / state identification

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ZHANG Shuai1, DING Xuexing1, WANG Shipeng2, LI Ning3, ZHANG Lanxia1. Study of dry gas seal friction lubrication state identification with improved DenseNet[J]. Journal of Vibration and Shock, 2025, 44(4): 313-321

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