Multi-source information separation of a hydroelectric generating set based on EEMD-SOBI

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Journal of Vibration and Shock ›› 2023, Vol. 42 ›› Issue (4) : 229-235.

ZHI Baoping1,2,3,QIN Jingjing1,2,3,YANG Chunjing1,2,3,YU Yang3

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Abstract

According to the Complex vibration element of hydropower unit, EEMD-SOBI method, namely, Ensemble Empirical Mode Decomposition-Second order blind source separation, is proposed to identify multisource vibration signals. The vibration source components are recognized by the primary decorrelation of the observation signals, the whitening calculation of the second-order statistics of each signal, then the compute of the diagonalization matrix, and the optimal estimation of the vibration source. the results show that the observation signals are used to decompose and identify basically the source information with insensitivity to noise. The existing problems for directly application of SOBI are the frequency diffusion, the non-full rank of coefficient matrix, the decorrelation preprocessing of observation signals and so on. The above problems are well resolved recently, so SOBI can be exploited for vibration source analysis of hydropower unit vibration testing.

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ZHI Baoping1,2,3,QIN Jingjing1,2,3,YANG Chunjing1,2,3,YU Yang3. Multi-source information separation of a hydroelectric generating set based on EEMD-SOBI[J]. Journal of Vibration and Shock, 2023, 42(4): 229-235

References

[1]贾嵘, 李涛涛, 夏洲, 马喜平. 基于随机共振和经验模态分解的水力发电机组振动故障诊断[J]. 水利学报, 2017，48(3):334-340.
Jia R, Li T T, Xia Z, Ma X P. Vibration fault diagnosis of hydroelectric generating unit by using stochastic resonance and Empirical Mode Decomposition [J]. Shui Li Xue Bao, 2017，48(3):334-340.
[2]Fu Q , Jing B , He P , et al. Fault Feature Selection and Diagnosis of Rolling Bearings Based on EEMD and Optimized Elman AdaBoost Algorithm[J]. IEEE Sensors Journal, 2018, PP(99):1-1.
[3]Han W Q , Gu A J , Zhou J . A Damage Modes Extraction Method from AE Signal in Composite Laminates Based on DEEMD [J]. Journal of Nondestructive Evaluation, 2019, 38(3).
[4]谭冬梅, 陈方望, 周强, 吴浩. 基于CEEMDAN-SOBI对桥梁监测挠度的分离研究[J]. 中国安全生产科学技术, 2019,15(11):123-129.
Tan D M, Chen F W, Zhou Q, Wu H. Study on separation of bridge monitoring deflection based on CEEMDAN-SOBI [J]. Journal of Safety Science and Technology, 2019,15(11):123-129.
[5]李康强, 冯志鹏, 孙宏图,等. 基于延拓EMD和GDE识别瞬时阻尼比的行星齿轮箱故障诊断[J]. 振动与冲击, 2021, 40(24):29-35.
Li K Q, Feng Z P, Sun H T, et al. Planetary gearbox fault diagnosis via extension EMD and GDE to identify instantaneous damping ratio[J]. Journal of Vibration and Shock, 2021, 40(24):29-35.
[6]周建民, 王发令, 张臣臣,等. 基于特征优选和GA-SVM的滚动轴承智能评估方法[J]. 振动与冲击, 2021, 40(4):227-234.
Zhou J M, Wang F L, Zhang C C, et al. An intelligent method for rolling bearing evaluation using feature optimization and GA-SVM[J]. Journal of Vibration and Shock, 2021, 40(4):227-234.
[7]张建伟, 侯鸽, 赵瑜, 马晓君，暴振磊. 排列熵算法在水工结构损伤诊断中的应用[J]. 振动.测试与诊断, 2018, 38(184-02):22-27+202.
Zhang J W, Hou G, Ma X J, Bao Z L. Damage Diagnosis of Hydraulic Structure Based on Permutation Entropy [J]. Journal of Vibration, Measurement & Diagnosis, 2018, 38(184-02):22-27+202.
[8]郭鹏程, 孙龙刚, 李辉, 罗兴锜. 基于多重分形谱和改进BP神经网络的水电机组振动故障诊断研究[J]. 水力发电学报, 2014, 33(3):299-305.
Guo P C, Sun L G, Li H, Luo X Q. Vibration fault diagnosis of hydropower unit based on multi-fractal spectrum and improved BP neural network [J]. Journal of Hydroelectric Engineering, 2014, 33(3):299-305.
[9]王丹丹, 周宇, 叶庆卫, 王晓东. 基于谱聚类的振动多模态信号幅谱分割研究与应用[J]. 中国机械工程, 2013(13):19-23.
Wang D D, Zhou Y, Ye Q W, Wang X D. Spectrum Segmentation of Multi-mode Vibration Signals Based on Spectral Clustering [J]. China Mechanical Engineering, 2013(13):19-23.
[10]李卫鹏, 曹岩, 李丽娟. 正交小波变换k-中心点聚类算法在故障诊断中的应用[J]. 振动与冲击, 2021, 40(7): 291-296.
Li W P, Cao Y, Li L J. Orthogonal wavelet transform KCA in fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(7):291-296.
[11]卢丹. 影响挖掘机驾驶员振动舒适性的座椅振源识别研究[D]. 2016. 山东大学.
Lu D. Research on Identification of Seat Vibration Source Affecting Excavator Driver`s Vibration Comfort [D], 2016, Shandong University.
[12] 王奉涛, 薛宇航, 王雷,等. 基于流形学习的滚动轴承故障盲源分离方法[J]. 振动.测试与诊断, 2020, 40(1):43-47+202
Wang F T, Xue Y H, Wang L, et al. Blind Source Separation Method for Rolling Bearing Faults Based on Manifold Learning [J]. Journal of Vibration，Measurement ＆ Diagnosis, 2020, 40(1):43-47+202.
[13]Belouchrani A, Abed-Meraim K, Cardoso J F, Moulines E. A blind source separation technique using second-order statistics [J]. IEEE Transactions on Signal Processing, 1997, 45(2):434-444.