Abstract:The vibration signal, of coal mine machinery when overloading, often has the nonlinear and non-stationary characteristics. It contains much information of running status of equipment and all manner of ambient noise mixed up with, so the spectrum analysis can’t be carried out directly. However, the EMD method has a certain advantage in dealing with the nonlinear and non-stationary signal, which is an adaptive signal processing method. According to the characteristics of coal mine machinery vibration signal, the de-noising method was proposed based on empirical mode decomposition (EMD). Firstly, decomposing the mechanical vibration signal based on EMD, to obtain the intrinsic mode function (IMF). Secondly, calculating the correlation coefficient of the IMF and the original signal and sorting them from smallest to largest. Then, calculating the maximum difference between two adjacent correlation coefficients to get the sensitive IMF, which realized filtering of the non-stationary signal and offered a good theoretical foundation for late fault diagnosis of mechanical equipment. And through the experimental data analysis, the effectiveness and feasibility of the EMD method for vibration signal de-noising is verified.
张大伟,马宏伟,曹现刚,李从会. 基于EMD的振动信号去噪方法研究[J]. 振动与冲击, 2016, 35(22): 38-40.
ZHANG Dawei, MA Hongwei, CAO Xiangang, LI Conghui. Study on Vibration Signal De-noising Method Based on Empirical Mode Decomposition. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(22): 38-40.
[1] Mallat S G. A theory for multiresolution signal decomposition: The wavelet represen- tation. IEEE Transactions on Pattern Recon- gition and Machine Intelligence, 1989, 11(7): 674-693.
[2] 王建国, 吴林峰, 秦绪华. 基于自相关分析和 LMD 的滚动轴承振动信号故障特征提取[J]. 中国机械工程, 2014, 25(002): 186-191.
Wang Jianguo,Wu Linfeng, Qin Xuhua. Rolling bearing vibration signal fault feature extraction based on autocorrelation analysis and EMD [J]. China Mechanical Engineering, 2014, 25(002): 186-191.
[3] 籍永建,王红军.基于EMD的主轴振动信号去噪方法研究[J].组合机床与自动化加工技术,2015(5):35-37.
JI Yongjian, WANG Hongjun. Research of denoising method for spindle vibration signal based on empirical mode decomposition [J]. Modular Machine Tool& Automatic Manufacturing Technique, 2015(5):35-37.
[4] 谭善文,秦树人,汤宝平.Hilbert-Huang变换的滤波特性及其应用[J].重庆大学学报,2004,27(2):9-12.
TAN Shanwen, QIN Shuren, TANG Baoping. The filtering character of Hilbert-Huang transform and its application [J]. Journal of Chongqing University, 2004,27(2):9-12.
[5]翟哲. EMD方法在近红外光谱信号去噪问题中的应用[D].大庆:黑龙江大学,2014.
ZHAI Zhe. The application of empirical mode decomposition method in near infrared spectroscopy for signal denoising [D]. Daqing: Heilongjiang University, 2014.
[6] 刘畅,周川,伍星等.基于广义形态滤波和相关系数的Hilbert-Huang变换方法[J].机械科学与技术,2011,30(1):71-75.
LIU Chang, ZHOU Chuan, WU Xing, etc. An improved Hibert-Huang transform based on generalized morphological filter and correlation coefficients [J]. Mechanical Science and Technology for Aerospace Engineering, 2011,30(1):71-75.
[7] 雷亚国. 基于改进 Hilbert-Huang 变换的机械故障诊断[J]. 机械工程学报, 2011, 47(5): 71-77.
LEI Yaguo. Machinery fault diagnosis based on improved Hilbert-Huang transform [J]. Journal of Mechanical Engineering, 2011, 47(5): 71-77.