[1]杨永灿,刘 韬,柳小勤,等.基于注意力机制的一维卷积神经网络行星齿轮箱故障诊断[J].机械与电子,2021,(10):3-8.
　YANG Yongcan,LIU Tao,LIU Xiaoqin,et al.Fault Diagnosis of Planetary Gearbox Based on One-dimensional Convolutional Neural Network with Attention Mechanism[J].Machinery & Electronics,2021,(10):3-8.
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基于注意力机制的一维卷积神经网络行星齿轮箱故障诊断()
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机械与电子[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2021年10期
页码:
3-8
栏目:
设计与研究
出版日期:
2021-10-24

文章信息/Info

Title:
Fault Diagnosis of Planetary Gearbox Based on One-dimensional Convolutional Neural Network with Attention Mechanism
文章编号:
１００１- ２２５７ ( ２０２１ ) １０-０００３-０６
作者:
杨永灿; 刘 韬; 柳小勤; 王廷轩; 王振亚
昆明理工大学机电工程学院,云南 昆明 ６５０５００
Author(s):
YANG Yongcan ;  LIU Tao ;  LIU Xiaoqin ;  WANG Tingxuan ;  WANG Zhenya
( Faculty of Mechanical and Electrical Engineering , Kunming University of Science and Technology , Kunming ６５０５００ , China )
关键词:
行星齿轮箱; 故障诊断; 卷积神经网络; 注意力机制
Keywords:
planetary gearbox ;  fault diagnosis ;  convolutional neural network ;  attentional mechanism
分类号:
TH１６５. ３ ; TP１８３
文献标志码:
A
摘要:
针对行星齿轮箱故障信号成分复杂和时变性强的特点,提出了基于注意力机制的一维卷积神经网络(１D-CNN )行星齿轮箱故障诊断方法.首先,将行星齿轮箱各类故障状态的原始振动信号进行分段处理,作为模型的输入;其次,利用一维卷积神经网络对行星齿轮箱的原始振动信号学习齿轮故障特征,结合注意力机制( AM )对特征序列自适应的赋予不同的权重,增强故障特征信息;最后,利用 Softmax 分类器实现行星齿轮箱的故障诊断.通过故障实验验证以及与其他模型的对比,该故障诊断模型具有较强的学习能力,诊断性能优于其他的深度学习模型,有较好的工程实际意义.
Abstract:
Aiming at the characteristics of complex signal components and strong time-variability of planetary gearbox fault , a one-dimensional convolutional neuraln etwork fault diagnosis model for planetary gearbox based on attention mechanism was designed and implemented． Firstly , the originalvibration signals of planetary gearbox in various fault states are processed in sections as the input of the model． Secondly,a one-dimensional convolutional neural network was used to extract the fault features from the original vibration signals of planetary gears , and the Attention Mechanism ( AM ) is used to self-adaptively assign different weights to the feature sequences to enhance the fault feature information． Finally , the fault diagnosis of planetary gearbox is realized by Softmax classifier．Through verification by failure experiments and comparison with other models , it is found that the fault diagnosis model has a strong learning ability , and its diagnostic performance is better than other deep learning models , and has good engineering practical significance．

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: ２０２１-０６-１０
基金项目:国家自然科学基金资助项目( ５２０６５０３０ ; ５１８７５２７２ )
作者简介:杨永灿 ( １９９６－ ),男,云南大理人,硕士研究生,研究方向为设备状态监测;刘 韬 ( １９８０－ ),男,云南昆明人,副教授,研究方向为设备状态监测与健康评估、机器学习在信号处理中的应用等,通信作者.
更新日期/Last Update: 2021-11-02