LIU Peijin, ZHANG Xiangxiang, SUN Yu, SHI Mengtao, HE Ning
(School of Mechatronic Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China)
Abstract: A signal pre-processing method based on optimal variational mode decomposition (OVMD) is proposed to improve the efficiency and accuracy of local data filtering and analysis of edge nodes in distributed electromechanical systems. Firstly, the singular points of original signals are eliminated effectively by using the first-order difference method. Then the OVMD method is applied for signal modal decomposition. Furthermore, correlation analysis is conducted to determine the degree of correlation between each mode and the original signal, so as to accurately separate the real operating signal from noise signal. On the basis of theoretical analysis and simulation, an edge node pre-processing system for distributed electromechanical system is designed. Finally, by virtue of the signal-to-noise ratio (SNR) and root-mean-square error (RMSE) indicators, the signal pre-processing effect is evaluated. The experimental results show that the OVMD-based edge node pre-processing system can extract signals with different characteristics and improve the SNR of reconstructed signals. Due to its high fidelity and reliability, this system can also provide data quality assurance for subsequent system health monitoring and fault diagnosis.
Key words: distributed electromechanical system; electromechanical signal; edge node; optimal variational mode decomposition(OVMD); signal pre-processing system
References
[1]Gu Y, Lin J. Mechanical fault diagnosis boosts manufacturing in China-visit Shaanxi Province mechanical product quality assurance and diagnosis key laboratory director Professor Lin Jing Aviation Manufacturing Technology, 2016, 501 (6): 22-23.
[2]Liu W Q, Xu L Y, Chen W P. Brittleness measurement and evaluation analysis of manufacturing equipment based on brittle risk entropy. Computer Integrated Manufacturing System, 2019, 25(11): 2820-2830.
[3]Ren Y, Li W, Zhu Z, et al. A new fault feature for rolling bearing fault diagnosis under varying speed conditions. Advances in Mechanical Engineering, 2017, 9(6):162-182.
[4]Lei Y G, Jia F, Kong D T, et al. Opportunities and challenges of mechanical intelligent troubleshooting under big data. Journal of Mechanical Engineering, 2018, 54 (5): 94-104.
[5]Xu X L. An overview of the information technology of electromechanical system condition monitoring and fault warning. Journal of Electronic Measurements and Instruments, 2016, 30 (3): 325-332.
[6]Liu Y J, Yao E T, Xu H Z. Hydraulic pump fault diagnosis based on particle filtering and self-regression spectrum. Chinese Journal of Scientific Instrument, 2012, 33 (3): 561-567.
[7]Lin J, Zhao M. Review and prospect of dynamic signal analysis methods of mechanical equipment at variable speed. China Science: Technical Science, 2015(7): 669-686.
[8]Wu J Z, Tao Y. Fan blade crack damage detection based on short-term Fourier transformations. China Journal of Engineering Machinery, 2014, 12 (2): 180-183.
[9]Zeng J, Chen Y F, Yang P, et al. A review of faultdiagnosis of large wind turbines. Grid Technology, 2018, 42(3): 849-860.
[10]Zhao X P, Zhao X L, Hou R T, et al. A new transientfrequency estimation algorithm for vibration signals in the vibration phase of rotating machinery. Journal of Mechanical Engineering, 2011, 47(7): 103-108.
[11]Yu K, Luo Z T, Li H F, et al. General parameterized synchrosqueezing transform and its application in rotating machinery vibration signal. Journal of Mechanical Engineering, 2019, 55 (11): 149-159.
[12]Feng L F, Gao J M, Gao Z Y, et al. The complex electromechanical system based on LP and wave packet monitoring sequence chaotic noise reduction method. Vibrations and Shocks, 2020, 39 (7): 1-7.
[13]Lin C Y, Zhang G Y, Li Y D, et al. A demodulation algorithm for MFSK signals based on SCD and wavelet transform. Science Technology and Engineering, 2013, 13(28): 8293-8298.
[14]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454: 903-995.
[15]Xie W J, Qi R, Xiao L, et al. Electromechanical dynamic system failure feature extraction based on EMD method. Computer Engineering and Applications, 2014,50(3):247-249.
[16]Wu Z H, Norden E H. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, 2011, 1(1): 1-41.
[17]Han J, Mirko V D B. Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding. Geophysics, 2015, 80(6): KS69-KS80.
[18]Dragomiretskiy K, Zosso D. Variational Mode Decomposition. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[19]Ji Z Y, Tang H. First-order differential and wavelength analysis of GPS observation data rough difference detection. Science and Technology and Engineering, 2013, 13(27): 8206-8210.
[20]Qing Z S, Gao Y P, Wu C, et al. Feature extraction method of ball mill load based on the adaptive variational mode decomposition and the improved power spectrum analysis. Chinese Journal of Scientific Instrument, 2020, 41 (5): 234-241.
[21]Shi Y, Wang Y J, Mei Y, et al. VMD harmonic detection method based on WPT and parameter optimization. Electrometry and instrumentation1-6.2020-07-30.http://kns.cnki.net/kcms/detail/23.1202.
[22]Jiao J J. Research on the fault diagnosis method of rolling bearing based on VMD and flow learning. Shenyang: Shenyang University of Technology, 2019: 24-28.
[23]Albert A P, NII A O. A criterion for selecting relevant intrinsic mode function in empirical mode decomposition. Advances in Adaptive Data Analysis, 2010, 2(1): 1-24.
分布式机电系统边缘节点信号预处理方法及应用设计
刘沛津, 张向向, 孙昱, 石梦涛, 贺宁
(西安建筑科技大学 机电工程学院, 陕西 西安 710055)
摘要:为提高分布式机电系统边缘节点对本地数据过滤和分析的效率及准确性, 提出了一种基于最优变分模式分解(Optimal variational mode decomposition, OVMD)的信号预处理方法。 首先, 利用一阶差分方法有效消除了原始信号中的奇异点, 随之利用最优变分模态分解方法对信号进行模态分解, 然后进行相关分析, 确定各模式与原始信号的相关程度, 从而从噪声信号中准确分离出真实的工作信号。 在理论与仿真分析基础上, 设计开发了分布式机电系统边缘节点预处理系统, 并采用信噪比及均方根误差指标评价信号预处理效果。 实验结果表明, 该机电信号预处理方法及设计的边缘节点预处理系统能够提取不同特征的信号, 提高重构信号信噪比, 具有较高的保真性和可靠性, 为后续系统健康监测、故障诊断等工作提供了数据保障。
关键词:分布式机电系统; 机电信号; 边缘节点; 最优变分模态分解; 信号预处理系统
引用格式:LIU Peijin, ZHANG Xiangxiang, SUN Yu, et al. Signal pre-processing method and application design of edge nodes for distributed electromechanical system. Journal of Measurement Science and Instrumentation, 2021, 12(3): 272-280. DOI: 10.3969/j.issn.1674-8042.2021.03.004
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