Fault diagnosis method based on image shape feature and LLTSA

ZHANG Qian-tu1,2,FANG Li-qin,2

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (9) : 172-177.

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Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (9) : 172-177.

Fault diagnosis method based on image shape feature and LLTSA

  • ZHANG Qian-tu1,2,FANG Li-qin,2
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Abstract

Aiming at the fault diagnosis problem of rolling bearing, a fault diagnosis method based on image shape feature and liner local tangent space alignment (LLTSA) was proposed. Firstly, the time waveform was transformed to snowflake image in polar coordinate space by symmetrized dot pattern (SDP) method, and image shape feature was initially extracted on the basis of analyzing the characteristic of the image. Secondly, the LLTSA was introduced to compress the initial high-dimension feature into the low-dimension which has better discrimination. Finally, the support vector machine (SVM) was employed to classify and evaluate the low-dimension feature. The experiment results of rolling bearing fault diagnosis shown that the image shape feature can represent the bearing state, and the LLTSA decreases the complexity of the feature data and enhance its clustering effect. Furthermore, a relatively high identification rate of four bearing states, namely, 100% was obtained by SVM, validated the effectiveness of the proposed method.

Key words

symmetrized dot pattern / shape feature / liner local tangent space alignment / support vector machine / fault diagnosis

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ZHANG Qian-tu1,2,FANG Li-qin,2. Fault diagnosis method based on image shape feature and LLTSA[J]. Journal of Vibration and Shock, 2016, 35(9): 172-177

References

[1] Xu Li, A’nan Zheng, Xunan Zhang, etal. Rolling element bearing fault detection using support vector machine with improved ant colony optimization[J]. Measurement, 2013, 46: 2726-2734.
[2] 唐贵基, 邓飞跃, 何玉灵, 等. 基于时间-小波能谱熵的滚动轴承故障诊断研究[J]. 振动与冲击, 2014, 33(7): 68-72,91.
TANG Gui-ji, DENG Fei-yue, HE Yu-ling, etal. Rolling element bearing fault diagnosis based on time-wavelat energy spectrum entropy[J]. Journal of Vibration and Shock, 2014, 33(7): 68-72,91.
[3] 程利军, 张英堂, 李志宁, 等. 基于时频分析及阶比跟踪的曲轴轴承故障诊断研究[J]. 振动与冲击, 2012, 31(19): 73-78.
CHENG Li-jun, ZHANG Ying-tang, LI Zhi-ning, etal. Fault diagnosis of an engine's main bearing based on time-frequency analysis and order tracking[J]. Journal of Vibration and Shock, 2012, 31(19): 73-78.
[4] 窦唯, 刘占生. 旋转机械故障诊断的图形识别方法研究[J]. 振动与冲击, 2012, 31(17): 171-175.
DOU Wei, LIU Zhan-sheng. A fault diagnosis method based on graphic recognition for rotating machine[J]. Journal of Vibration and Shock, 2012, 31(17): 171-175.
[5] 张立国, 杨瑾, 李晶, 等. 基于小波包和数学形态学结合的图像特征提取方法[J]. 仪器仪表学报, 2010, 31(10): 2285-2290.
ZHANG Liguo, YANG Jin, LI Jing, etal. Image characteristic extraction method based on wavelat packet and mathematicalm orphology[J]. Chinese Journal of Scientific Instrument, 2010, 31(10): 2285-2290.
[6] Shibata K, Takahashi A, Shirai T. Fault diagnosis of rotating machinery through visualization of sound signals[J]. Mechanical Systems and Signal Processing, 2000, 14(2): 229-241.
[7] Wu Jian da, Chuang Chao qin. Fault diagnosis of internal combustion engines using visual dot patterns of acoustic and vibration signals[J]. NDT Int., 2005, 38(8): 605-614.
[8] Delvecchio S, D'Elia G, Mucchi E, etal. Advanced signal processing tools for the vibratory surveillance of assembly faults in diesel engine cold tests[J]. ASME Journal of Vibration and Acoustics, 2010, 132: 1-10.
[9] Kouropteva O, Okun O, Pietikainen M. Supervised locally linear embedding algorithm for pattern recognition[J]. Pattern Recognition and Image Analysis, 2003, 2652(9): 386-394.
[10] He X F, Yan S C, Hu Y X, etal. Face recognition using laplacianfaces[J]. IEEE Trans.Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340.
[11] Zhang T H, Yang J, Zhao D L, etal. Linear local tangent space alignment and application to face recognition [J]. Neurocomputing, 2007, 70: 1547-1553.
[12] 杨成, 冯焘, 王中方, 等. 基于SDP法诊断发动机的异响[J]. 声学技术, 2010, 29(5): 523-527.
YANG Cheng, FENG Tao, WANG Fang-zhong, etal. Abnormal noise diagnosis of motorcycle engines based on Symmetrized Dot Pattern method[J]. Technical Acoustics, 2010, 29(5): 523-527.
[13] 任玲辉, 刘凯, 张海燕. 基于图像处理技术的机械故障诊断研究进展[J]. 机械设计与研究, 2011, 27(05): 21-24.
REN Ling-hui, LIU Kai, ZHANG Hai-yan. Progress on mechanical fault diagnosis based on image processing[J]. Machine Design and Research, 2011, 27(05): 21-24.
[14] 秦襄培, 郑贤中. Matlab数字图像处理宝典[M]. 北京:电子工业出版社. 2011.
[15] 李锋, 汤宝平, 陈法法. 基于线性局部切空间排列维数化简的故障诊断[J]. 振动与冲击, 2012, 31(13): 36-40.
LI Feng, TANG Bao-ping, CHEN Fa-fa. Fault diagnosis model based on dimension reduction using linear local tangent space alignment[J]. Journal of Vibration and Shock, 2012, 31(13): 36-40.
[16] 向丹, 葛爽. 一种基于小波包样本熵和流行学习的故障特征提取模型[J]. 振动与冲击, 2014, 33(11): 1-5.
 XIANG Dan, GE Shuang. A model of fault feature extraction based on wavelet packet sample entropy and manifold learning[J]. Journal of Vibration and Shock, 2014, 33(11): 1-5.
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