基于自学习字典的盲提取方法在滚动轴承多故障诊断中的应用
A Blind Source Extraction Method based on Self-learned Dictionary and Its Application in Fault Diagnosis of Bearing Multi-type Fault
- 机械传动 2022年46卷第2期 页码：149-154
- 作者机构：
1.贵州民族大学 机械电子工程学院， 贵州 贵阳 550025
- 作者简介：
程兴国（1970— ），男，湖北汉川人，工学博士，副教授；主要从事传感器技术及应用、智能制造研究。
- 基金信息：贵州民族大学引进人才科研基金（2015）贵州民族大学校级教研项目(GUN2016JG09)
DOI：10.16578/j.issn.1004.2539.2022.02.024
中图分类号：
扫 描 看 全 文
程兴国,翁璞.基于自学习字典的盲提取方法在滚动轴承多故障诊断中的应用[J].机械传动,2022,46(02):149-154.
Cheng Xingguo,Weng Pu.A Blind Source Extraction Method based on Self-learned Dictionary and Its Application in Fault Diagnosis of Bearing Multi-type Fault[J].Journal of Mechanical Transmission,2022,46(02):149-154.
程兴国,翁璞.基于自学习字典的盲提取方法在滚动轴承多故障诊断中的应用[J].机械传动,2022,46(02):149-154. DOI： 10.16578/j.issn.1004.2539.2022.02.024.
Cheng Xingguo,Weng Pu.A Blind Source Extraction Method based on Self-learned Dictionary and Its Application in Fault Diagnosis of Bearing Multi-type Fault[J].Journal of Mechanical Transmission,2022,46(02):149-154. DOI： 10.16578/j.issn.1004.2539.2022.02.024.
摘要
当齿轮箱中的多个轴承同时发生故障时，由于各故障源之间的相互耦合效应，常规盲提取方法难以对其进行有效特征提取。为解决上述问题，提出一种基于稀疏表征自学习字典理论的盲提取方法。首先，将稀疏表征自学习字典方法用于滚动轴承多故障信号分析，得到一系列自学习字典集；然后，利用学习到的字典集重构滚动轴承多故障信号以消除噪声及干扰信号；最后，将盲提取方法用于重构后的滚动轴承复合故障信号，抽取出滚动轴承各单一故障信号，再逐一对单一故障信号进行包络解调分析，以获取相应的故障特征。通过实验，对所述方法的可行性及有效性进行了验证。
Abstract
When multiple bearings in a gearbox failure simultaneously，conventional blind source extraction （BSE） on the vibration signals of bearing multi-type faults would not be ideal due to the mutual coupling effect among each of the fault sources. A BSE based on sparse representation self-learned dictionary method is proposed to solve the above problem.Firstly，apply the self-learned sparse dictionary originating from sparse representation on the multi-type faults vibration signals directly and a set of self-learning dictionaries are obtained.Then，the multi-type faults vibration signals are re-constructed basing on the obtained learned dictionary to eliminate noise and interference signals.Finally，apply the BSE method on compound fault signals of reconstructed rolling bearings，each single fault signal of rolling bearing is extracted，and then the envelope demodulation analysis is carried out one by one to obtain the corresponding fault characteristics.Feasibility and effectiveness of the proposed method are verified through experiment.
关键词
自学习字典盲提取滚动轴承多故障诊断
Keywords
Self-learned dictionaryBlind source extractionRolling bearingMulti-type faults diagnosis
references
WU Z H，HUANG N.Ensemble empirical mode decomposition：a noise assisted data analysis method［J］.Advance in Adaptive Data Analysis，2009（1）：1-41.
ANTONI J.The spectral kurtosis：a useful tool for characterising non-stationary signals［J］.Mechanical Systems and Signal Processing，2004，20（2）：282-307.
ANTONI J.Fast computation of the kurtogram for the detection of transient faults［J］.Mechanical Systems and Signal Processing，2005，21（1）：108-124.
SINGH K S，KUMAR S，DWIVEDI J P.Compound fault prediction of roling bearing using multimedia data［J］.Multimed Tools Applications，2017，76（18）：18771-18788.
ŽVOKELJ M，ZUPAN S，PREBIL I.Multivariate and multiscale monitoring of large-size low-speed bearings using ensemble empirical mode decomposition method combined with principal component analysis［J］.Mechanical Systems and Signal Processing，2009，24（4）：1049-1067.
PURUSHOTHAM V，NARAYANAN S，PRASAD A N.Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden markov model based fault recognition［J］.NDT and E International，2005，38（8）：654-664.
ABBASION S，RAFSANJANI A，FARSHIDIANFAR A，et al.Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine［J］.Mechanical Systems and Signal Processing，2007，21（7）：2933-2945.
LEI Y G，HE Z J，ZI Y Y.Application of a novel hybrid intelligent method to compound fault diagnosis of locomotive roller bearings ［J］.ASME Journal of Vibration and Acoustics，2008，130（3）：034501：1-6.
LI Z X，YAN X P，TIAN Z，et al.Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis［J］.Measurement，2013，46（1）：259-271.
WANG T Y，CHU F L，HAN Q K，et al.Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods［J］.Journal of Sound and Vibration，2017，392：367-381.
ZHAO D Z，LI J Y，CHENG W D，et al.Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed［J］.Journal of Sound and Vibration，2016，378：109-123.
YANG P L，FOMEL S.Seislet-based morphological component analysis using scale-dependent exponential shrinkage［J］.Journal of Applied Geophysics，2015，118：66-74.
WEN J，ZHAO J S，WANG C L.Improved morphological component analysis for interference hyperspectral image decomposition［J］.Computers and Electrical Engineering，2015，46：394-402.
练秋生，石保顺，陈书贞.字典学习模型、算法及其应用研究进展［J］.自动化学报，2015，41（2）：240-260.
LIAN Qiusheng，SHI Baoshun，CHEN Shuzhen.Research advances on dictionary learning models，algorithms and applications［J］.Acta Automatica Sinica，2015，41（2）：240-260.
FENG Z P，ZHOU Y K，ZUO M J，et al.Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis：a review with examples［J］.Measurement，2017，103：106-132.
DING J M，ZHAO W T，MIAO B R.Adaptive sparse representation based on circular-structure dictionary learning and its application in wheelset-bearing fault detection［J］.Mechanical Systems and Signal Processing，2018，111：399-422.
赵乐，杨绍普，刘永强，等.基于稀疏分解和频域相关峭度的轴承微弱故障特征提取［J］.振动与冲击，2019，38（23）：196-202，212.
ZHAO Le，YANG Shaopu，LIU Yongqiang，et al.Weak fault feature extraction of bearing based on sparse decomposition and frequency domain correlation kurtosis［J］.Journal of Vibration and Shock，2019，38（23）：196-202，212.
FENG Z P，LIANG M.Complex signal analysis for planetary gearbox fault diagnosis via shift invariant dictionary learning［J］.Measurement，2016，90：382-395.
LI J M，YAO X F，WANG H，et al.Periodic impulse extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis［J］.Mechanical Systems and Signal Processing，2019，126：568-589.
WANG Z Y，CHEN J，DONG G M，et al.Constrained independent component analysis and its application to machine fault diagnosis ［J］.Mechanical Systems and Signal Processing，2011，25：2501-2512.
GROSSE R，RAINA R，KWONG H，et al.Shift-invariant sparse coding for audio classification［C］.Proceedings of the Twenty-Third Conference on Uncertainty in AI，2007：149-158.
0
浏览量
3
下载量
0
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
- 技术支持由北京北大方正电子有限公司提供 豫ICP备05013835号-5
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies