识别车轴疲劳裂纹声发射信号方法

By constructing a CNN-BiLSTM network model to process the acoustic emission signal of the axle, the problem of interference signal influence in the identification of fatigue cracks in the axle was solved, and higher identification accuracy and intelligent identification effect were achieved.

CN117110446BActive Publication Date: 2026-07-17DALIAN JIAOTONG UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
DALIAN JIAOTONG UNIVERSITY
Filing Date
2023-08-09
Publication Date
2026-07-17

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Abstract

本发明提供一种识别车轴疲劳裂纹声发射信号方法，包括：获取安装在车轴处的声发射传感器采集的声发射信号；对声发射信号进行预处理，并向声发射信号添加属性标签得到处理后的声发射信号并将其划分为训练集和测试集；构建CNN‑BiLSTM网络模型，根据训练集对CNN‑BiLSTM网络模型进行训练；将测试集输入训练后的CNN‑BiLSTM网络模型，训练后的CNN‑BiLSTM网络模型将测试集分类为疲劳裂纹信号和非疲劳裂纹信号。通过CNN‑BiLSTM网络模型的卷积神经网络和双向长短期记忆网络对车轴混合有干扰信号的声发射信号进行识别分类，以实现对车轴疲劳裂纹的检测。
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