Local Oscillatory-Characteristic Decomposition and Its Application to Roller Bearing Fault Diagnosis

PDF(2174 KB)

Journal of Vibration and Shock ›› 2016, Vol. 35 ›› Issue (1) : 89-95.

ZHANG Kang SHI Yangchun TANG Mingzhu WU Jiateng

Author information +

History +

Abstract

A new self-adaptive time-frequency analysis method named local oscillatory-characteristic decomposition (LOD) is proposed. This method is based on local oscillatory characteristics of signal itself, and it uses the operations including differential, coordinates domain transform and piecewise linear transform to decompose the signal into a series of mono-oscillatory components (MOC) which instantaneous frequency has physical meanings, and thus especially suitable for processing the multi-component signals. On the basis of illustrating the decomposition principle of LOD in detail, the LOD is compared with the empirical mode decomposition (EMD) and Local mean decomposition (LMD) by analyzing the simulated signals, and the results show the superiorities of LOD. Meanwhile, aiming at the multi-component modulated feature of roller bearing fault vibration signals, the LOD is applied to the roller bearing fault diagnosis. The analytical results from the experimental roller bearing signals demonstrate that the LOD can extract the fault characteristics of roller bearing fault vibration signals effectively.

Key words

nonstationary signal / local oscillatory-characteristic decomposition / mono-oscillatory components / roller bearing / fault diagnosis

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHANG Kang SHI Yangchun TANG Mingzhu WU Jiateng. Local Oscillatory-Characteristic Decomposition and Its Application to Roller Bearing Fault Diagnosis[J]. Journal of Vibration and Shock, 2016, 35(1): 89-95

References

[1] J Rafiee, P W Tse. Use of autocorrelation of wavelet coefficients for fault diagnosis[J]. Mechanical Systems and Signal Processing, 2009, 23(5): 1554-1572.
[2] Jian-Da Wu, Peng-Hsin Chiang. Application of Wigner–Ville distribution and probability neural network for scooter engine fault diagnosis[J]. Expert Systems with Applications, 2009, 36(2): 2187-2199.
[3] Huang N E, Shen Z and Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society A, 1998, 454: 903-995.
[4] Fangji Wu, Liangsheng Qu. Diagnosis of subharmonic faults of large rotating machinery based on EMD[J]. Mechanical Systems and Signal Processing, 2009, 23(2): 467-475.
[5] Cheng Junsheng, Yu Dejie, Yang Yu. Application of SVM and SVD technique based on EMD to the fault diagnosis of the rotating machinery[J]. Shock and Vibration, 2009, 16(1): 89-98.
[6] X H He, X G Hua, Z Q Chen, et al. EMD-based random decrement technique for modal parameter identification of an existing railway bridge[J]. Engineering Structures, 2011, 33(4): 1348-1356.
[7] Yuo-Hsien Shiau, Ming-Chya Wu. Detecting characteristics of information masked by a laser-triggered microwave system via Hilbert-Huang transform[J]. Optics Communications, 2010, 283(9): 1909-1916.
[8] G G S Pegram, M C Peel, T A McMahon. Empirical mode decomposition using rational splines: an application to rainfall time series[J]. Proceedings of the Royal Society A, 2008, 464: 1483-1501.
[9] WU Zhaohua，HUANG N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proc. R. Soc. Lond. A，2004，460(3): 1597-1611.
[10] Fangji Wu, Liangsheng Qu. An improved method for restraining the end effect in empirical mode decomposition and its applications to the fault diagnosis of large rotating machinery[J]. Journal of Sound and Vibration, 2008, 314(3-5): 586-602.
[11] Jonathan S Smith. The local mean decomposition and its application to EEG perception data[J]. Journal of the Royal Society Interface, 2005, 2(5): 443-454.
[12] 程军圣, 杨宇, 于德介. 局部均值分解方法及其在齿轮故障诊断中的应用[J]. 振动工程学报, 2009, 22(1): 76-84.
    Cheng Jun-sheng, Yang Yu, Yu Dejie, The local mean decomposition method and its application to gear fault diagnosis[J]. Journal of Vibration Engineering, 2009, 22(1): 76-84.
[13] 李慧梅, 安钢, 黄梦. 基于局部均值分解的边际谱在滚动轴承故障诊断中的应用[J]. 振动与冲击, 2014, 33(3): 5-8.
Li Huimei, An Gang, Huang Meng. Application of marginal spectrum based on local mean decomposition in rolling bearing fault diagnosis[J]. Journal of Vibration and Shock, 2014, 33(3): 5-8.
[14] Wang Yanxue, He Zhengjia, Zi Yanyang. A demodulation method based on local mean decomposition and its application in rub-impact fault diagnosis[J]. Measurement Science and Technology, 2009, 20(2): 1-10.
[15] Wang Yanxue, He Zhengjia, Zi Yanyang. A comparative study on the local mean decomposition and empirical mode decomposition and their applications to rotating machinery health diagnosis[J]. Journal of vibration and acoustics, 2010, 132(2): 1-10.
[16] Junsheng Cheng, Yi Yang, Yu Yang. A rotating machinery fault diagnosis method based on local mean decomposition[J]. Digital Signal Processing, 2012, 22(2): 356-366.
[17] 任达千. 基于局域均值分解的旋转机械故障特征提取方法及系统研究[D]. 杭州: 浙江大学, 2008.
Ren Daqian. Study on Methods and System for Fault Characteristics Extraction of Rotating Machines Based on Local Mean Decomposition[D]. Hangzhou: Zhejiang University, 2008.
[18] 张亢. 局部均值分解方法及其在旋转机械故障诊断中的应用研究[D]. 长沙: 湖南大学, 2012.
    Zhang Kang. Research on Local Mean Decomposition Method and Its Application to Rotating Machinery Fault Diagnosis[D]. Changsha, Hunan University, 2012.
[19] Rilling G, Flandrin P. Sampling effects on the empirical mode decomposition[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 43-59.
[20] Rilling G, Flandrin P, Goncalves P. On empirical mode decomposition and its algorithms[C]. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing, Grado, June, 2003: 8-11.
[21] 胡维平, 杜明辉. 信号采样率对经验模态分解的影响研究[J]. 信号处理, 2007, 23(4): 637-640.
    Hu Weiping, Du Minghui. The limitation of sampling for the empirical mode decomposition[J]. Signal Processing, 2007, 23(4): 637-640.
[22] 张亢, 程军圣, 杨宇. 基于自适应波形匹配延拓的局部均值分解端点效应处理方法[J]. 中国机械工程, 2010, 21(4): 457-462.
Zhang Kang, Cheng Junsheng, Yang Yu. Processing method for end effects of local mean decomposition based on self-adaptive waveform matching extending[J]. China Mechanical Engineering, 2010, 21(4): 457-462.