|本期目录/Table of Contents|

[1]张海龙,袁德成.基于多尺度CNN-BiLSTM的轴承故障诊断[J].工业仪表与自动化装置,2022,(03):75-78+84.[doi:10.19950/j.cnki.cn61-1121/th.2022.03.016]
　ZHANG Hailong,YUAN Decheng.Bearing fault diagnosis based on multi-scale CNN-BiLSTM[J].Industrial Instrumentation & Automation,2022,(03):75-78+84.[doi:10.19950/j.cnki.cn61-1121/th.2022.03.016]
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基于多尺度CNN-BiLSTM的轴承故障诊断

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年03期
页码:
75-78+84
栏目:
出版日期:
2022-06-15

文章信息/Info

Title:
Bearing fault diagnosis based on multi-scale CNN-BiLSTM
文章编号:
1000-0682（2022）03-0000-00
作者:
张海龙; 袁德成
沈阳化工大学 信息工程学院, 辽宁 沈阳 110142
Author(s):
ZHANG Hailong;  YUAN Decheng
School of information engineering, Shenyang University of Chemical Technology, Liaoniang Shenyang 110142, China
关键词:
轴承; 故障诊断; 多尺度卷积神经网络; 双向长短时记忆网络
Keywords:
bearing;  fault diagnosis;  multi-scale convolutional neural network;  two-way long and short-term memory network
分类号:
TH165+.3TH133.33
DOI:
10.19950/j.cnki.cn61-1121/th.2022.03.016
文献标志码:
A
摘要:
针对传统故障诊断方法提取特征不丰富、未充分利用时序特征的问题，提出了一种基于多尺度CNN和BiLSTM融合的滚动轴承故障诊断方法。首先设计多尺度CNN模型进行多尺度特征信息的提取；其次设计BiLSTM模型进行提取特征前后之间的内部关系；最后通过全连接层构建了特征信息与故障类型的映射，通过softmax分类器输出故障诊断结果。以准确率为评价指标，该方法在多负载场景下诊断准确率为99.2%，在变负载场景下诊断平均准确率为89.6%。实验结果表明，该方法具有良好的自适应工况的能力。
Abstract:
Aiming at the problem that the traditional fault diagnosis methods are not rich in feature extraction and the time series feature is not fully utilized, a rolling bearing fault diagnosis method based on multi-scale CNN and BiLSTM is proposed. First, design a multi-scale CNN model to extract multi-scale feature information; secondly, design a BiLSTM model to extract the internal relationship between before and after the feature; finally, build a mapping between feature information and fault types through a fully connected layer, and output fault diagnosis through the softmax classifier result. Taking the accuracy as an evaluation index, the method has a diagnostic accuracy rate of 99.2% in a multi-load scenario, and an average diagnostic accuracy rate of 89.6% in a variable load scenario. The experimental results show that this method has a good ability to adapt to working conditions.

参考文献/References:

[1] L?Y, ZHOUI Q, LI Y, et al. A predictive maintenance system for multi-granularity faults based on AdaBelief-BP neural network and fuzzy decision making[J]. Advanced Engineering Informatics, 2021, 49: 101318.

[2] TIAN Y, LIU X. A deep adaptive learning method for rolling bearing fault diagnosis using immunity[J]. Tsinghua Science and Technology, 2019, 24(6): 750-762.
[3] XIAO Y ,FEI D , DING E , et al. Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection[J]. IEEE Access, 2018, 6(99):3715-3730.
[4] 张韦,张永,骈晓琴,等.基于改进EMD样本熵和SVM的风机滚动轴承故障诊断[J].机电工程技术,2021,50(12):38-41+67.
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[6] DING X, HE Q. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(8): 1926-1935.
[7] LEVENT E . Bearing Fault Detection by One-Dimensional Convolutional Neural Networks[J]. Mathematical Problems in Engineering,2017:1-9.
[8] WEI Z , PENG G , LI C , et al. A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals[J]. Sensors, 2017,17(3):425.[9] CAO L , ZHANG J , WANG J Y , et al. Intelligent fault diagnosis of wind turbine gearbox based on Long short-term memory networks[C]// 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). IEEE, 2019.
[10] 郑直,张华钦,潘月.基于改进鲸鱼算法优化LSTM的滚动轴承故障诊断[J].振动与冲击,2021,40(07): 274-280.
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备注/Memo

备注/Memo:
收稿日期：2021-12-31

基金项目：
国家重点研发计划（2018YFB1700200）

作者简介：
张海龙（1996），男，河北唐山人，硕士研究生，主要从事故障诊断以及预测性维护的研究。

通信作者：
袁德成（1960），男，内蒙古阿拉善人，教授，博士，主要从事复杂模型建模、控制与优化的研究。
更新日期/Last Update: 1900-01-01