基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断

樊家伟,郭瑜,伍星,陈鑫,林云

振动与冲击 ›› 2021, Vol. 40 ›› Issue (20) : 271-277.

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振动与冲击 ›› 2021, Vol. 40 ›› Issue (20) : 271-277.
论文

基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断

  • 樊家伟,郭瑜,伍星,陈鑫,林云
作者信息 +

Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement

  • FAN Jiawei, GUO Yu, WU Xing, CHEN Xin, LIN Yun
Author information +
文章历史 +

摘要

针对支持向量机、深度学习等人工智能算法在齿轮箱故障诊断应用上的不足,提出一种基于长短时记忆(long short-term memory,LSTM)神经网络和故障特征增强的行星齿轮箱故障智能诊断方法。该方法对行星齿轮箱不同局部故障振动信号进行滑动加窗截取,对截取的每段信号分别做快速傅里叶变换并选取包含故障特征丰富的频段实现对故障特征的增强,并以该数据作为输入对LSTM神经网络进行训练,通过训练完成的LSTM神经网络模型智能提取所选频段内的故障特征并实现行星齿轮箱不同局部故障的识别诊断。试验结果表明该方法可以有效诊断行星齿轮箱不同局部故障,并能提高网络模型的故障识别率。

Abstract

To address the shortcomings of Support Vector Machine, deep learning and other artificial intelligence algorithms in the application of gearbox fault diagnosis, an intelligent fault diagnosis method of planetary gearboxes based on the long short-term memory (LSTM) neural network and the fault feature enhancement was proposed.In the proposed method,a sliding window was used to intercept the vibration signals of different local faults of the planetary gearboxes at first.Then, each of the intercepted signals was transformed through the Fast Fourier Transform and the frequency band rich in fault features was selected to enhance the fault features.The data from previous step were used as input to train the LSTM neural network.Finally, the trained LSTM neural network model was used to intelligently extract the fault features in the selected frequency band and achieve identification as well as diagnosis of different local faults of planetary gearboxes.The experimental results show that the proposed method can effectively diagnose different local faults of the planetary gearboxes with better fault recognition accuracy of the network model.

关键词

行星齿轮箱 / 故障特征增强 / LSTM神经网络 / 故障诊断

Key words

planetary gearbox / fault feature enhancement / long Short term memory / fault diagnosis

引用本文

导出引用
樊家伟,郭瑜,伍星,陈鑫,林云. 基于LSTM神经网络和故障特征增强的行星齿轮箱故障诊断[J]. 振动与冲击, 2021, 40(20): 271-277
FAN Jiawei, GUO Yu, WU Xing, CHEN Xin, LIN Yun. Fault diagnosis of planetary gearboxes based on LSTM neural network and fault feature enhancement[J]. Journal of Vibration and Shock, 2021, 40(20): 271-277

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