Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism

Gao Yunduan; Tian Ye; Zhu Yongbo; Li He; Zhang Jinjie

doi:10.16578/j.issn.1004.2539.2023.03.015

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Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism

Development·Application | 更新时间：2023-03-29

- Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism
- Journal of Mechanical Transmission Vol. 47, Issue 3, Pages: 105-112(2023)
- 作者机构：
  
  1.中国航空工业集团公司北京长城航空测控技术研究所，北京 100176
  2.北京化工大学高端机械装备健康监控与自愈化北京市重点实验室，北京 100029
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2023.03.015
  CLC：
- Published：15 March 2023，
  
  Received：23 February 2022，
  
  Revised：08 April 2022，
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高云端,田野,朱永波等.基于稀疏注意力机制的齿轮早期磨损故障诊断研究[J].机械传动,2023,47(03):105-112.

Gao Yunduan,Tian Ye,Zhu Yongbo,et al.Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism[J].Journal of Mechanical Transmission,2023,47(03):105-112.
高云端,田野,朱永波等.基于稀疏注意力机制的齿轮早期磨损故障诊断研究[J].机械传动,2023,47(03):105-112. DOI： 10.16578/j.issn.1004.2539.2023.03.015.

Gao Yunduan,Tian Ye,Zhu Yongbo,et al.Research on Gear Early Wear Fault Diagnosis Based on the Sparse Attention Mechanism[J].Journal of Mechanical Transmission,2023,47(03):105-112. DOI： 10.16578/j.issn.1004.2539.2023.03.015.

摘要

在齿轮故障诊断领域中，对齿轮早期磨损故障实现有效诊断具有重要意义。然而，早期磨损故障特征弱，诊断难度大。针对该问题，提出了一种基于稀疏注意力机制的齿轮早期磨损故障诊断模型，采用一种新的稀疏注意力机制结合卷积神经网络，改进传统分段序列注意力机制，实现了具体故障频率定位。应用齿轮箱故障模拟实验数据进行测试验证，相比其他诊断方法，所提方法能够在同等样本条件与计算代价下，实现更为准确全面的诊断，降低分析成本，获得敏感故障特征频率，为齿轮维护提供数据支撑。

Abstract

In the field of gear fault diagnosis

it is of great significance to effectively diagnose early wear faults of gears. However

early wear fault has weak characteristics and is difficult to diagnose. Thus

a gear early wear fault diagnosis model based on the sparse attention mechanism is proposed to solve this problem. This model uses a new sparse attention mechanism combined with a convolution neural network to improve the traditional sectional attention mechanism and locate the specific fault frequency. The gearbox fault simulation test data is used for test verification. Compared with other diagnosis methods

this new method can achieve a more accurate and comprehensive diagnosis

reduce analysis cost and obtain sensitive fault characteristic frequency under the same sample condition and calculation cost. The conclusion of this method can provide data support for gear maintenance.

关键词

齿轮磨损故障诊断卷积神经网络注意力机制

Keywords

Gear wearFault diagnosisConvolution neural networkAttention mechanism

references

丁康，朱小勇，陈亚华.齿轮箱典型故障振动特征与诊断策略［J］.振动与冲击，2001（3）：9-14.

DING Kang，ZHU Xiaoyong，CHEN Yahua.Vibration characteristics and diagnosis strategy of typical faults of gearbox［J］.Journal of Vibration and Shock，2001（3）：9-14.

蔡连捷，何正洋.基于VMD技术的船用齿轮断齿信号的分析与诊断［J］.中国设备工程，2021（22）：39-40.

CAI Lianjie，HE Zhengyang.Analysis and diagnosis of broken tooth signal of marine gear based on VMD technology［J］.China Plant Engineering，2021（22）：39-40.

李晓虎，贾民平，许飞云.频谱分析法在齿轮箱故障诊断中的应用［J］.振动、测试与诊断，2003（3）：14-16.

LI Xiaohu，JIA Minping，XU Feiyun.A new unbiased frequency response function estimator in experimental modal analysis［J］.Journal of Vibration，Measurement & Diagnosis，2003（3）：14-16.

刘秀丽，徐小力.基于特征金字塔卷积循环神经网络的故障诊断方法［J］.上海交通大学学报，2022，56（2）：182-190.

LIU Xiuli，XU Xiaoli.Fault diagnosis method based on feature pyramid CRNN network［J］.Journal of Shanghai Jiaotong University，2022，56（2）：182-190.

向玲，王朋鹤，李京蓄.基于CNN-LSTM的风电机组异常状态检测［J］.振动与冲击，2021，40（22）：11-17.

XIANG Ling，WANG Penghe，LI Jingxu.Abnormal state detection of wind turbines based on CNN-LSTM［J］.Journal of Vibration and Shock，2021，40（22）：11-17.

王辉，徐佳文，严如强.基于深度宽卷积Q网络的行星齿轮箱故障智能诊断方法［J］.仪器仪表学报，2022，43（3）：109-120.

WANG Hui，XU Jiawen，YAN Ruqiang.Intelligent fault diagnosis for the planetary gearbox based on the deep wide convolution Q network［J］.Chinese Journal of Scientific Instrument，2022，43（3）：109-120.

马芸婷，张超，王宇晨.PCA-SAE的齿轮箱故障诊断方法研究［J］.机械设计与制造，2022（3）：144-147.

MA Yunting，ZHANG Chao，WANG Yuchen.Research on gearbox fault diagnosis method of PCA-SAE［J］.Machinery Design & Manufacture，2022（3）：144-147.

YE Z，YU J.Deep morphological convolutional network for feature learning of vibration signals and its applications to gearbox fault diagnosis［J］.Mechanical Systems and Signal Processing，2021，161（5）：107984.

ZHANG K，TANG B，DENG L，et al.A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox［J］.Measurement，2021，179（10）：109491.

张智禹，尹爱军，谭建.融合注意力机制的改进DBN变工况齿轮箱故障诊断方法［J］.振动与冲击，2021，40（14）：47-52.

ZHANG Zhiyu，YIN Aijun，TAN Jian.Improved DBN method with attention mechanism for the fault diagnosis of gear boxes under varying working condition［J］.Journal of Vibration and Shock，2021，40（14）：47-52.

孔子迁，邓蕾，汤宝平，等.基于时频融合和注意力机制的深度学习行星齿轮箱故障诊断方法［J］.仪器仪表学报，2019，40（6）：221-227.

KONG Ziqian，DENG Lei，TANG Baoping，et al.Fault diagnosis method of deep learning planetary gearbox based on time-frequency fusion and attention mechanism［J］.Chinese Journal of Scientific Instrument，2019，40（6）：221-227.

ZHENG X，WU J，YE Z.An end-to-end CNN-BiLSTM attention model for gearbox fault diagnosis［C］//2020 IEEE International Conference on Progress in Informatics and Computing（PIC）.IEEE，2020：386-390.

余浩帅，汤宝平，张楷，等.小样本下混合自注意力原型网络的风电齿轮箱故障诊断方法［J］.中国机械工程，2021，32（20）：2475-2481.

YU Haoshuai，TANG Baoping，ZHANG Kai，et al.Fault diagnosis method of wind power gearbox based on hybrid self attention prototype network with small samples［J］.China Mechanical Engineering，2021，32（20）：2475-2481.

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，2021，39（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，2021，39（10）：3-8.

樊家伟，郭瑜，伍星，等.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，et al.Fault diagnosis of planetary gearbox based on LSTM neural network and fault feature enhancement［J］.Journal of Vibration and Shock，2021，40（20）：271-277.

ZHAO M，ZHONG S，FU X，et al.Deep residual shrinkage networks for fault diagnosis［J］.IEEE Transactions on Industrial Informatics，2020，16（7）：4681-4690.

JIANG J，LI H，MAO Z，et al.A digital twin auxiliary approach based on adaptive sparse attention network for diesel engine fault diagnosis［J］.Scientific Reports，2022，12（1）：675.

李欣倩，杨哲，任佳.基于互信息与层次聚类双重特征选择的改进朴素贝叶斯算法［J］.测控技术，2022，41（2）：36-40.

LI Xinqian，YANG Zhe，REN Jia.Improved naive Bayes algorithm based on dual feature selection of mutual information and hierarchical clustering［J］.Measurement & Control Technology，2022，41（2）：36-40.

周开乐，杨善林，丁帅，等.聚类有效性研究综述［J］.系统工程理论与实践，2014，34（9）：2417-2431.

ZHOU Kaile，YANG Shanlin，DING Shuai，et al.On cluster validation［J］.Systems Engineering-Theory & Practice，2014，34（9）：2417-2431.

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