基于声发射及WST-CNN协同的滑动轴承润滑状态识别

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (22) : 71-77.

论文

卢绪祥，刘顺顺，陈向民，张亢

作者信息 +

Identification of the lubrication state of journal bearings based on acoustic emission and WST-CNN collaboration

LU Xuxiang，LIU Shunshun，CHEN Xiangmin，ZHANG Kang

Author information +

文章历史 +

摘要

为实现声发射信号对滑动轴承润滑状态变化进行灵敏表征，提出一种采用小波散射变换及卷积神经网络结合的滑动轴承润滑状态识别及故障诊断研究方法。以某310MW汽轮发电组滑动轴承现场试验所得声发射信号为研究对象，将现有小波散射网络加入散射路径优化机制并进行参数优化，对滑动轴承声发射信号进行自动鲁棒特征提取，将最佳特征矩阵输入优化后的卷积神经网络进行润滑状态识别分类。结果表明：优化后的小波散射网络能够有效提取声发射信号特征，结合优化后的卷积神经网络对特征矩阵进行智能识别，对滑动轴承润滑状态识别率可达到95.28%，能够高效精确地对滑动轴承润滑状态进行诊断。

Abstract

A method for lubrication state identification and fault diagnosis of journal bearings based on wavelet scattering transform and convolutional neural network was proposed to use acoustic emission signal to sensitively characterize the differential lubrication states of journal bearings. The optimized wavelet scattering network was used for automatic robust feature extraction of the acoustic emission signals of a journal bearing of a 310MW turbo-generator set, and the best feature matrix was input into the optimized convolutional neural network for lubrication state recognition and classification. The results show that the optimized wavelet scattering network can effectively extract the acoustic emission signal features, and combined with the optimized convolutional neural network to intelligently identify the feature matrix, the recognition rate of the lubrication states of the sliding bearing can reach 95.28%, and the lubrication states of the sliding bearing can be efficiently and accurately diagnosed.

导出引用

卢绪祥，刘顺顺，陈向民，张亢. 基于声发射及WST-CNN协同的滑动轴承润滑状态识别[J]. 振动与冲击, 2023, 42(22): 71-77

LU Xuxiang，LIU Shunshun，CHEN Xiangmin，ZHANG Kang. Identification of the lubrication state of journal bearings based on acoustic emission and WST-CNN collaboration[J]. Journal of Vibration and Shock, 2023, 42(22): 71-77

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