基于CEEMDAN-CNN-BiLSTM的行星齿轮箱故障诊断分析

Analysis of Planetary Gearbox Fault Diagnosis Based on CEEMDAN-CNN-BiLSTM

阐述一种基于自适应噪声完备集合经验模态分解(CEEMDAN)相关系数、卷积神经网络(CNN)以及双向长短时记忆网络(BiLSTM)的行星齿轮箱故障诊断方法,用以解决行星齿轮箱振动信号受噪声干扰难以有效提取故障特征的问题,提高故障诊断的准确率。

This paper expounds a fault diagnosis method for planetary gearbox based on adaptive noise complete set empirical mode decomposition(CEEMDAN)correlation coefficient,convolutional neural network(CNN),and bidirectional long short time memory network(BiLSTM),to solve the problem of difficulty in effectively extracting fault features from planetary gearbox vibration signals due to noise interference and improve the accuracy of fault diagnosis.