A Fault Feature Extraction Method Based on ITD-SVD and MOMEDA

doi:10.3969/j.issn.1007-7375.2021.06.007

Abstract

Abstract: In order to improve the accuracy of rolling bearing fault diagnosis, a fault feature extraction method based on the combination of intrinsic time scale decomposition (ITD), singular value decomposition (SVD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed. Firstly, ITD is used to decompose the fault vibration signal, and the component signal is filtered and reconstructed according to the combination of kurtosis and correlation coefficient weight index screening criteria. Then the SVD filter is used to reduce the noise. Finally, the periodic impact components in the noise reduced signal are extracted by MOMEDA, and the diagnosis results are obtained by Hilbert envelope analysis. Through the analysis of experimental data, the results show that the proposed method can not only filter out noise interference, enhancing fault feature information, but also extract fault features accurately.

Key words: intrinsic time scale decomposition, singular value decomposition (SVD), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA), fault feature extraction

CLC Number:

TP18

YANG Jingzong, YANG Tianqing, WU Limei. A Fault Feature Extraction Method Based on ITD-SVD and MOMEDA[J]. Industrial Engineering Journal, 2021, 24(6): 48-56.

References

[1] 李益兵, 王磊, 江丽. 基于PSO改进深度置信网络的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(5): 89-96
LI Yibing, WANG Lei, JIANG Li. Rolling bearing fault diagnosis based on DBN algorithm improved with PSO[J]. Journal of Vibration and Shock, 2020, 39(5): 89-96
[2] 杨磊, 杨帆, 何艳. 采用样本熵-完备经验模态分解的脑电信号眼电伪迹去除算法[J]. 西安交通大学学报, 2020, 54(8): 1-9
YANG Lei, YANG Fan, HE Yan. EOG artifacts removal algorithm for EEG signals based on sample entropy-complete ensemble empirical mode decomposition with adaptivenoise[J]. Journal of Xi'an Jiaotong University, 2020, 54(8): 1-9
[3] HUANG N E, SHEN Z, LONG S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non -stationary time series analysis[C/OL]. (1998-03-08). https://royalsocietypublishing.org/doi/10.1098/rspa.1998.0193.
[4] 赵书涛, 马莉, 朱继鹏, 等. 基于CEEMDAN样本熵与FWA-SVM的高压断路器机械故障诊断[J]. 电力自动化设备, 2020, 40(3): 181-186
ZHAO Shutao, MA Li, ZHU Jipeng, et al. Mechanical fault diagnosis of high voltage circuit breaker based on CEEMDAN sample entropy and FWA-SVM[J]. Electric Power Automation Equipment, 2020, 40(3): 181-186
[5] 张文斌, 江洁, 俞利宾, 等. 互补集合经验模式分解与奇异值能量谱在风电齿轮故障识别中的应用[J]. 太阳能学报, 2020, 41(2): 137-143
ZHANG Wenbin, JIANG Jie, YU Libin, et al. Application of complementary ensemble empirical mode decomposition and singular value energy spectrum in wind power gear fault identification[J]. Acta Energiae Solaris Sinica, 2020, 41(2): 137-143
[6] 邓勇, 胡徐松. 基于EMD和SPS的容差模拟电路故障诊断[J]. 电子测量与仪器学报, 2020, 34(2): 67-72
DENG Yong, HU Xusong. Fault diagnosis of tolerance analog circuit based on EMD and SPS method[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(2): 67-72
[7] 胡茑庆, 陈徽鹏, 程哲, 等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报, 2019, 55(7): 9-18
HU Niaoqing, CHEN Huipeng, CHEN Zhe, et al. Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks[J]. Journal of Mechanical Engineering, 2019, 55(7): 9-18
[8] YAN Jihong, LU Lei. Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis[J]. Signal Processing, 2014, 98: 74-87
[9] CHENG Junsheng, YU Dejie, TANG Jiashi. Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis[J]. Mechanism and Machine Theory, 2008, 43(6): 712-723
[10] WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J/OL]. (2009-01-01). https://www-worldscientific-com-s.vpn.seu.edu.cn:8118/doi/abs/10.1142/S1793536909000047.
[11] FREI M G, OSORIO I. Intrinsic time-scale decomposition: time-frequency energy analysis and real-time filtering of non-stationary signals[J]. Royal Society of London Proceedings, 2007, 463: 321-342
[12] 张朝林, 范玉刚, 冯早. ITD-多尺度熵和ELM的风电轴承健康状态识别[J]. 机械科学与技术, 2018, 37(11): 1731-1736
ZHANG Zhaolin, FAN Yugang, FENG Zao. Health status recognition of wind turbine bearings based on ITD-MSE and ELM[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(11): 1731-1736
[13] 任东方, 张涛, 韩洁. 结合ITD与非线性分析的通信辐射源个体识别方法[J]. 信号处理, 2018, 34(3): 331-339
REN Dongfang, ZHANG Tao, HAN Jie. Approach of specific communication emitter identification combining ITD and nonlinear analysis[J]. Journal of Signal Processing, 2018, 34(3): 331-339
[14] 赵雄鹏, 潘宏侠, 刘广璞, 等. 应用ITD分形模糊熵的自动机早期故障诊断[J]. 机械设计与制造, 2019(1): 134-137
ZHAO Xiongpeng, PAN Hongxia, LIU Guangpu, et al. Application of ITD fractal fuzzy entropyin automaton early fault diagnosis[J]. Machinery Design & Manufacture, 2019(1): 134-137
[15] PENG S, LIAO Y H, LIN J. The shock pulse index and its application in the fault diagnosis of rolling element bearings[J]. Sensors, 2017, 17(3): 535-560
[16] SMITH W A, RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study[J]. Mechanical Systems and Signal Processing Method, 2015, 64–65: 100-131