Diagnosis Method for Sparse Optimization Based on Multi-step Grid Search of Bearing Coupling Faults

Gong Xiaoyun; Li Chao; Zhao Zhiwei; Ning Xiaobo; Zhang Yuxiang; Han Ming

doi:10.16578/j.issn.1004.2539.2022.10.023

您当前的位置：

首页 >

文章列表页 >

Diagnosis Method for Sparse Optimization Based on Multi-step Grid Search of Bearing Coupling Faults

Development·Application | 更新时间：2022-11-05

- Diagnosis Method for Sparse Optimization Based on Multi-step Grid Search of Bearing Coupling Faults
- Journal of Mechanical Transmission Vol. 46, Issue 10, Pages: 149-155(2022)
- 作者机构：
  
  1.郑州轻工业大学机电工程学院，河南郑州 450002
  2.河南中烟工业有限责任公司安阳卷烟厂，河南安阳 455004
  3.巩义市建设机械制造有限公司，河南巩义 451200
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2022.10.023
  CLC：
扫描看全文
巩晓赟,李超,赵志伟等.轴承耦合故障的多步网格搜索优化稀疏诊断方法[J].机械传动,2022,46(10):149-155.

Gong Xiaoyun,Li Chao,Zhao Zhiwei,et al.Diagnosis Method for Sparse Optimization Based on Multi-step Grid Search of Bearing Coupling Faults[J].Journal of Mechanical Transmission,2022,46(10):149-155.
巩晓赟,李超,赵志伟等.轴承耦合故障的多步网格搜索优化稀疏诊断方法[J].机械传动,2022,46(10):149-155. DOI： 10.16578/j.issn.1004.2539.2022.10.023.

Gong Xiaoyun,Li Chao,Zhao Zhiwei,et al.Diagnosis Method for Sparse Optimization Based on Multi-step Grid Search of Bearing Coupling Faults[J].Journal of Mechanical Transmission,2022,46(10):149-155. DOI： 10.16578/j.issn.1004.2539.2022.10.023.

摘要

转子系统不平衡-轴承耦合故障下，不平衡故障特征与轴承故障相互调制，弱故障特征易被强故障特征和强噪声成分淹没。通过稀疏表示最优参数的选取，减少稀疏重构误差，提出一种多步网格搜索优化稀疏诊断方法，进一步提高了稀疏表示算法的抗干扰能力。首先，基于多步网格搜索理论，建立多步网格搜索优化稀疏表示方法模型，实现计算精度的提高；其次，通过优化位移因子和频率因子，实现基于自适应Gabor原子字典正交匹配追踪算法的信号重构；最后，对不同信噪比的耦合仿真故障数据进行分析。结果表明，随噪声干扰增大，所提方法的抗干扰能力增强。不同实验案例进一步验证了所提方法的准确性和适用性。

Abstract

Under the unbalance-bearing coupling fault of the rotor system, the unbalance fault features and bearing faults modulate each other, and the weak fault features are easily submerged by the strong fault features and strong noise components. Thus, a multi-step grid search optimization sparse diagnosis method is proposed to improve the anti-interference ability of sparse representation algorithm by selecting the optimal parameters of sparse representation to reduce the sparse reconstruction error. Firstly, based on the multi-step grid search theory, a multi-step grid search optimization sparse representation method model is established to improve the calculation accuracy. Secondly, the signal reconstruction based on adaptive Gabor atom dictionary orthogonal matching tracking algorithm is realized by optimizing the displacement factor and frequency factor. Finally, the coupling simulation fault data with different SNR are analyzed. The results show that the anti-interference ability of the proposed method increases with the increase of noise interference and different experimental cases further verify the accuracy and applicability of the proposed method.

关键词

转子系统耦合故障诊断稀疏表示网格搜索参数优化

Keywords

Rotor systemCoupling fault diagnosisSparse representationGrid searchParameter optimization

references

靳玉林，刘治汶，陈予恕.航空发动机双转子叶片-机匣碰摩故障模拟［J/OL］.航空学报，2021-04-29：https：//kns.cnki.net/kcms/detail/11.1929.V.20210429.1116.012.htmlhttps://kns.cnki.net/kcms/detail/11.1929.V.20210429.1116.012.html.

JIN Yulin，LIU Zhiwen，CHEN Yushu.Simulation of bladecasing rubbing for a dual-rotor system of an aero-engine［J］.Acta Aeronautica et Astronautica Sinica，2021-04-29：https：//kns.cnki.net/kcms/detail/11.1929.V.20210429.1116.012.htmlhttps://kns.cnki.net/kcms/detail/11.1929.V.20210429.1116.012.html.

罗忠，王晋雯，韩清凯，等.组合支承转子系统动力学的研究进展［J］.机械工程学报，2021，57（7）：44-60.

LUO Zhong，WANG Jinwen，HAN Qingkai，et al.Review on dynamics of the combined support-rotor system［J］.Journal of Mechanical Engineering，2021，57（7）：44-60.

许琦，吴昊，赵立超，等.多跨转子系统耦合故障定量诊断方法［J］.振动工程学报，2015，28（3）：495-502.

XU Qi，WU Hao，ZHAO Lichao，et al.Quantitative coupling fault diagnosis method of multi-span rotor based on harmonic components［J］.Journal of Vibration Engineering，2015，28（3）：495-502.

胡超凡，王衍学.基于张量分解的滚动轴承复合故障多通道信号降噪方法研究［J］.机械工程学报，2019，55（12）：50-57.

HU Chaofan，WANG Yanxue.Research on multi- channel signal denoising method for multiple faults diagnosis of rolling element bearings based on tensor factorization［J］.Journal of Mechanical Engineering，2019，55（12）：50-57.

雷冰龙，李超，何康，等.共用支承-转子系统耦合振动分析及试验［J］.航空动力学报，2020，35（11）：2293-2305.

LEI Binglong，LI Chao，HE Kang，et al.Coupling vibration characteristics analysis and experiment of shared support-rotors system［J］.Journal of Aerospace Power，2020，35（11）：2293-2305.

陈是扦，彭志科，周鹏.信号分解及其在机械故障诊断中的应用研究综述［J］.机械工程学报，2020，56（17）：91-107.

CHEN Shiqian，PENG Zhike，ZHOU Peng.Review of signal decomposition theory and its applications in machine fault diagnosis［J］.Journal of Mechanical Engineering，2020，56（17）：91-107.

SUN R B，YANG Z B，ZHAI Z，et al.Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis［J］.Mechanical Systems and Signal Processing，2019，122：737-753.

YANG J Z，TSE P.Sparse representation of complex steerable pyramid for machine fault diagnosis by using non-contact video motion to replace conventional accelerometers［J］.Measurement，2021，175（365）：109104.

TANG H F，CHEN J，DONG G M.Sparse representation based latent components analysis for machinery weak fault detection［J］.Mechanical Systems and Signal Processing，2014，46（2）：373-388.

樊薇，李双，蔡改改，等.基于小波基稀疏信号特征提取的轴承故障诊断［J］.振动工程学报，2015，28（6）：972-980.

FAN Wei，LI Shuang，CAI Gaigai，et al.Wavelet sparse signal feature extraction method and its application in bearing fault diagnosis［J］.Journal of Vibration Engineering，2015，28（6）：972-980.

吴芳坦，林慧斌，何国林.齿轮局部故障的滑窗相关和重叠群稀疏诊断方法［J］.振动工程学报，2020，33（5）：1084-1093.

WU Fangtan，LIN Huibin，HE Guolin.Sliding window correlation and overlapping group sparsity diagnosis method for gear localized fault［J］.Journal of Vibration Engineering，2020，33（5）：1084-1093.

Baraniuk Richard G，Candes Emmanuel，Elad Michael，et al.Application of sparse representation and compressive sensing［J］.Proceedings of the IEEE，2010，98（6）：906-909.

王强，张培林，王怀光，等.基于多目标粒子群算法的稀疏分解参数优化［J］.振动与冲击，2017，36（23）：45-50.

WANG Qiang，ZHANG Peilin，WANG Huaiguang，et al.Parametric optimization of sparse decomposition based on multi-objective particle swarm optimization algorithm［J］.Journal of Vibration and Shock，2017，36（23）：45-50.

WANG J L，ZHANG J.Big data analytics for forecasting cycle time in semiconductor wafer fabrication system［J］.International Journal of Production Research，2016，54（23）：7231-7244.

PATI Y C，REZAIIFAR R，KRISHNAPRASAD P S.Orthogonal matching pursuit：recursive function approximation with applications to wavelet decomposition［C］//.Conference Record of the Asilomar Conference on Signals，Systems & Computers 1993，1：40-44.

LU Y F，XIE R，LIANG S Y.Adaptive online dictionary learning for bearing fault diagnosis［J］.The International Journal of Advanced Manufacturing Technology，2019，101：195-202.

LIU Z，WU X J，SHU Z Q.Sparsity augmented discriminative sparse representation for face recognition［J］.Pattern Analysis and Applications，2019，22：1527-1535.

CARDOSO G，SANJIE J.Data compression and noise suppression of ultrasonic NDE signals using wavelets［C］//IEEE Symposium on Ultrasonics.IEEE，2003：250-253.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Design and Research of an Eight-bar Back Lifting Mechanism with Single Degree of Freedom

Research and Test of X-wing Flapping-wing Mechanism Based on the Crank Slider Mechanism

Design and Analysis of Backplane Mechanism for New Nursing Bed Chair

Spatial Tooth Profile Design and Optimization Analysis of Involute Harmonic Gear

Vibration Characteristic Analysis and Feature Extraction of Bearing Coupling Fault based on Sparse Representation

Related Author

No data

Related Institution

School of Mechanical Engineering, Jiangsu University of Technology

School of Electrical Engineering, Guangxi University

College of Mechanical Engineering，Jiangsu University of Technology

Engineer Training Center， Tianjin University of Technology and Education

Tianjin Key Laboratory of Modern Mechatronics Equipment Technology， Tianjin Polytechnic University

⁰