[1]任世锦,李新玉,徐桂云,等.融合再加权奇异值分解与周期重叠簇稀疏的机械故障特征抽取算法[J].南京师范大学学报(自然科学版),2018,41(04):39.[doi:10.3969/j.issn.1001-4616.2018.04.008]
　Ren Shijin,Li Xinyu,Xu Guiyun,et al.A Machinery Fault Feature Extraction Approach IntegratingReweighted SVD with Periodic Overlapping Group Sparsity[J].Journal of Nanjing Normal University(Natural Science Edition),2018,41(04):39.[doi:10.3969/j.issn.1001-4616.2018.04.008]
点击复制

融合再加权奇异值分解与周期重叠簇稀疏的机械故障特征抽取算法()
分享到：

《南京师范大学学报》（自然科学版）[ISSN:1001-4616/CN:32-1239/N]

卷:
第41卷
期数:
2018年04期
页码:
39
栏目:
·数学与计算机科学·
出版日期:
2018-12-31

文章信息/Info

Title:
A Machinery Fault Feature Extraction Approach IntegratingReweighted SVD with Periodic Overlapping Group Sparsity
文章编号:
1001-4616(2018)04-0039-10
作者:
任世锦1; 李新玉2; 徐桂云2; 潘剑寒1; 杨茂云1
(1.江苏师范大学计算机学院,江苏 徐州 221116)(2.中国矿业大学机电工程学院,江苏 徐州 221116)
Author(s):
Ren Shijin1; Li Xinyu2; Xu Guiyun2; Pan Jianhan1; Yang Maoyun1
(1.School of Computer Science & Technology,Jiangsu Normal University,Xuzhou 221116,China)(2.School of Mechatronic Engineering,China University of Mining and Technology,Xuzhou 221116,China)
关键词:
再加权奇异值分解; 周期重叠簇稀疏; 机械故障诊断; 稀疏特征抽取; 周期调制强度
Keywords:
reweighted singular value decomposition(RSVD); periodic overlapping group sparsity(POGS); machinery fault diagnosis; sparse feature extraction; periodic modulation intensity(PMI)
分类号:
TP181
DOI:
10.3969/j.issn.1001-4616.2018.04.008
文献标志码:
A
摘要:
机械故障特征具有周期性、稀疏性以及被噪声污染严重特点,而大部分特征抽取方法(如局部线性嵌入(locally linear embedding,LLE)、局部切空间排列(local tangent space alignment,LTSA))性能往往受到噪声影响. 因此,抑制振动信号噪声、抽取有效故障特征成为机械故障检测的关键. 本文提出融合奇异值分解与周期重叠簇稀疏(reweighted singular value decomposition integrating with periodic overlapping group sparsity,RSVD-POGS)的机械故障稀疏特征抽取方法. 该方法首先利用RSVD把多成分振动信号分解为奇异成分集合,并使用周期调制强度(periodic modulation intensity,PMI)准则选择有效奇异成分,然后使用POGS从奇异成分提取稀疏周期冲击特征,并由选择的奇异成分重构原始信号,增强周期稀疏故障信号特征. 最后,使用低SNR仿真周期冲击信号对RSVD-POGS算法与POGS方法进行对比,并将RSVD-POGS方法应用于实验台轴承正常和故障信号的特征提取中. 实验结果表明,该算法可以有效地提取稀疏微弱故障特征,具有较大的优越性.
Abstract:
The machinery fault features are generally periodic,sparse and corrupted by heavy background noise. Most feature extraction methods like locally linear embedding(LLE),local tangent space alignment(LTSA)and etc.,are susceptible to noise. Therefore,signal denoising and faulty feature enhancement are critical for machinery fault detection. To address the issue,a reweighted singular value decomposition integrating with periodic overlapping group sparsity(RSVD-POGS)-based feature extraction approach is developed for machinery fault detection in this study. Firstly,RSVD is introduced to decompose multiple-component vibration signal into a set of singular components and periodic modulation intensity(PMI)criterion is utilized to select salient singular components. POGS is then conducted to enhance sparse and periodic impulse features hidden in the remained singular components,and the original signal can be constructed by the denoised singular components. The comparison between RSVD-POGS and POGS is carried out on low SNR simulated sparse periodic signal and RSVD-POGS is utilized to extract features rom normal and fault vibration signals collected from experimental platform. The experimental results demonstrate that the proposed method can effectively enhance the periodic and sparse fault features for machinery fault diagnosis.

参考文献/References:

[1] ZHAO M,JIA X D. A novel strategy for signal denoising using reweighted SVD and its application to weak fault enhancement of rotating machinery[J]. Mechanical systems and signal processing,2017,94:129-147.
[2]JIANG H M,CHEN J,DONG G M,et al. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis[J]. Mechanical systems and signal processing,2015,52/53:338-359.
[3]王聪. 基于稀疏表达的机械信号处理方法及其在滚动轴承故障诊新中的应用研究[D]. 合肥:中国科学技术大学,2017.
[4]YU F J,ZHOU F. Classification of machinery vibration signals based on group sparse representation[J]. Journal of vibration engineering,2016,18(3):1540-1554.
[5]YANG H G,LIN H B,DING K. Sliding window denoising ngular value decomposition and its application on rolling bearing impact fault diagnosis[J]. Journal of sound and vibration,2018,421:205-219.
[6]YUAN J,JI F,GAO Y,et al. Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection[J]. Mechanical systems and signal processing,2018,104:323-346.
[7]ZHANG G,YI T,ZHANG T Q,et al. A multiscale noise tuning stochastic resonance for fault diagnosis in rolling element bearings[J]. Chinese journal of physics,2018,56(1):145-157.
[8]CHEN J L,ZI Y Y,HE Z,et al. Adaptive redundant multiwavelet denoising with improved neighboring coefficients for gearbox fault detection[J]. Mechanical systems and signal processing,2013,38:549-568.
[9]Lü Y,YUAN R,SONG G B. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing[J]. Mechanical systems and signal processing,2016,81:219-234.
[10]HU A,YAN X A,XIANG L. A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension[J]. Renewable energy,2015,83:767-778.
[11]GOLAFSHAN R,SANLITURK K Y. SVD and Hankel matrix based denoising approach for bearing fault detection and its assessment using artificial faults[J]. Mechanical systems and signal processing,2016,70/71:36-50.
[12]LIU T,CHEN J,DONG G. Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis[J]. Journal of vibration and control,2015,21(8):1506-1521.
[13]CONG F,CHEN J,GONG M G,et al. Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis[J]. Mechanical systems and signal processing,2013,34:218-230.
[14]JIANG Y H,TANG B P,QIN Y,et al. Feature extraction method of wind turbine based on adaptive Morlet wavelet and SVD[J]. Renewable energy,2011,36:2146-2153.
[15]SANLITURK K Y,CAKAR O. Noise elimination from measured frequency response functions[J]. Mechanical systems and signal processing,2005,19:615-631.
[16]MURUGANATHAM B,SANJITH M,KUMAR B K,et al. Roller element bearing fault diagnosis using singular spectrum analysis[J]. Mechanical systems and signal processing,2013,35:150-166.
[17]ZHU K P,LIN X,LI K X,et al. Compressive sensing and sparse decomposition in precision machining process monitoring:from theory to applications[J]. Mechatronics,2015,31:3-15.
[18]TAN Y,HOU X S,CHEN Z,et al. Image compressive sensing reconstruction based on collaboration reduced rank preprocessing[J]. Electronics letters,2017,53(11):717-718.
[19]WEN F Q,ZHANG G,BEN D. Estimation of multipath parameters in wireless communications using multi-way compressive sensing[J]. Journal of systems engineering and electronics,2015,26(5):908-915.
[20]SHAO H D,JIANG H K,ZHANG H Z,et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical systems and signal processing,2018,100:743-765.
[21]SUN R B,YANG Z B,CHEN X F. Gear fault diagnosis based on the structured sparsity time-frequency analysis[J]. Mechanical systems and signal processing,2018,102:346-363.
[22]FAN W,CAI G G,ZHU Z K. Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction[J]. Mechanical systems and signal processing,2015,56/57:230-245.
[23]TANG G,YANG Q,WANG H Q,et al. Sparse classification of rotating machinery faults based on compressive sensing strategy[J]. Mechatronics,2015,31:60-67.
[24]张新鹏. 压缩感知及其在旋转机械健康监测中的应用[D]. 长沙:国防科技大学,2015.
[25]刘畅. 基于压缩感知的滚动轴承特征提取与特征约简方法研究[D]. 昆明:昆明理工大学,2017.
[26]HAN T,JIANG D X,SUN Y K,et al. Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification[J]. Measurement,2018,118:181-193.
[27]GANESAN V,DAS T,RAHNAVARD N,et al. Vibration-based monitoring and diagnosis using compressive sensing[J]. Journal of sound and vibration,2017,394:612-630.
[28]CHEN X F,DU Z H,LI J M,et al. Compressed sensing based on dictionary earning for extracting impulse components[J]. Signal processing,2014,96:94-109.
[29]CHEN G,LIN F,HUANG W. Sparse discriminant manifold projections for bearing fault diagnosis[J]. Journal of sound and vibration,2017,399:330-334.
[30]贺王鹏. 周期簇稀疏特征提取方法及其在机械故障诊断中的应用研究[D]. 西安:西安交通大学,2017.

备注/Memo

备注/Memo:
收稿日期:2018-08-16.
基金项目:国家自然科学基金(61703187、61773185).
通讯联系人:任世锦,副教授,研究方向:机器学习、过程与机械故障诊断. E-mail:sjren_phd@163.com
更新日期/Last Update: 2018-12-30