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Gas-film seal end face status identification based on deep fusion model

Published:2024-08-02 author:LIU Wei, ZHANG Shuyao, LI Shuangxi, et al. Browse: 612 Check PDF documents

Gas-film seal end face status identification based on

deep fusion model

LIU Wei1, ZHANG Shuyao1, LI Shuangxi1, MA Yabin2, LIANG Kunhai1

(1.Collage of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China;

2.Software R&D Department, Sage Energy Technology (Beijing) Co., Ltd., Beijing 100102, China)

Abstract: The air film sealing device is one of the most widely used sealing technologies in the industrial field, its reliable sealing performance is essential for the normal operation of the equipment. The relative movement of the contact end faces of the dynamic and static sealing rings of the gas film sealing device will produce friction, and the friction process will produce complex acoustic emission signals, which often imply important information about the operation status of the sealing end faces. It is often difficult to accurately identify and classify these weak characteristic signals by traditional methods. Therefore, it is necessary to develop fault diagnosis methods with higher precision. In order to solve the problem that it was difficult to identify the friction state of the dynamic and static ring end faces of mechanical seals, taking the air film sealing device as the research object, a method for identifying the end face of air film seals based on the deep fusion model was proposed. Firstly, the acoustic emission signal of the sealed end face was collected by the acoustic emission sensor and acquisition equipment. Secondly, the wavelet packet transform method was used to filter the collected signal and extract the weak features in the time domain and frequency domain, and then the deep random forest (DRF) was integrated into the convolutional neural network (CNN) as a classification layer. Finally, according to the leakage amount of the experiment, the confusion matrix and the receiver operating curve were used to analyze the feature extraction ability of the two models. The research results show that the accuracy of the CNN-DRF fusion model for the two features of the sealed end-face acoustic emission signal is respectively 96% and 98%, which can fully extract the signal feature information and has better fault diagnosis ability than the traditional CNN model.

Key words: air film sealing technology; mechanical seal; acoustic emission signal; wavelet packet transform method; fusion model; deep random forest (DRF); convolutional neural network (CNN); feature extraction; feature recognition accuracy

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