柴油机传动机构振动信号具有强耦合、强冲击及强干扰特征，准确的信号特征提取是振动源识别及抑制的关键。针对变分模态提取（Variational Mode Extraction，VME）方法在处理柴油机振动信号存在自适应性及准确性不足的问题，本研究以振动信号的峰值频率为中心频率的初始值，考虑分解信号与原始信号的相关性，以分解分量的峰峰值、均方根值及峰值因子为判定指标，提出一种参数自适应VME（Parameter Adaptive Variational Mode Extraction，PAVME）算法，并通过构建强干扰环境下柴油机振动特征的模拟信号验证了该方法的准确性和鲁棒性。基于PAVME和功率谱密度函数（Power Spectral Density Function, PSD）识别出齿轮箱异常振动源为齿轮啮合激励。综合激励源和传递路径两个维度考虑，最后提出了通过轴系扭振控制进行传动机构振动抑制的方案。
Abstract
The vibration signal transmission mechanism has strong coupling, strong impact, and strong interference characteristics for diesel engine. Accurate signal characteristic extraction is the key to identifying and suppressing vibration sources. To address the issues of adaptability and accuracy of the Variational Mode Extraction (VME) method in handling diesel engine vibration signals, the peak frequency of the vibration signal was taken as the initial value of the center frequency, the correlation between the decomposed signal and the original signal was considered, and the peak to peak value, root mean square value, and peak factor of the decomposed components were used as the judgment indicators, the parameter adaptive variable mode extraction (PAVME) was proposed, and the accuracy and applicability of PAVME was verified by constructing simulated signals of diesel engine vibration characteristics in low signal to noise ratio environments. the abnormal vibration source of the gearbox was identified as gear meshing excitation based on PAVME and Power Spectral Density Function (PSD). Taking into account both the source of excitation and the transmission path, a scheme for suppressing the vibration of the transmission mechanism through shaft torsional vibration control was proposed.
关键词
参数自适应变分模态提取 /
振动 /
柴油机 /
传动机构
{{custom_keyword}} /
Key words
Parameter Adaptive Variational Mode Extraction /
Vibration /
Diesel engine /
Transmission Mechanism
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 丁康,李巍华,朱小勇.齿轮及齿轮箱故障诊断实用技术[M].北京:机械工业出版社, 2005.
Ding Kang, Li Weihua, Zhu Xiaoyong. Practical Technology for Fault Diagnosis of Gears and Gearboxes [M]. Beijing: Machinery Industry Press, 2005
[2] Zheng X, Zhou N, Zhou Q, et al. Experimental Investigation on the High-frequency Pressure Oscillation Characteristics of a Combustion Process in a DI Diesel Engine [J]. Energies, 2020, 13(4):1:25.
[3] Dragomiretskiy K, Zosso D. Variational Mode Decomposition [J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544.
[4] Yao J, Xiang Y, Qian S, et al. Noise source identification of diesel engine based on variational mode decomposition and robust independent component analysis [J]. Applied Acoustics, 2017, 116: 184-194.
[5] 周小龙, 徐鑫莉, 王尧等. 基于变分模态分解和最大重叠离散小波包变换的齿轮信号去噪方法 [J]. 振动与冲击, 2021, 40(12): 265-274+289.
Zhou Xiaolong, Xu Xinli, Wang Yao, A gear signal denoising method based on variational mode decomposition and maximum overlap discrete wavelet packet transform [J] Vibration and Shock, 2021, 40 (12): 265-274+289
[6] Li X, Ma Z, Kang D, et al. Fault diagnosis for rolling bearing based on VMD-FRFT [J]. Measurement, 2020, 155: 107554.
[7] 赵昕海, 张术臣, 李志深等. 基于VMD的故障特征信号提取方法[J]. 振动. 测试与诊断, 2018, 038(001): 11-19.
Zhao Xinhai, Zhang Shuchen, Li Zhishen, Fault feature signal extraction method based on VMD [J] Vibration Testing and Diagnosis, 2018, 038 (001): 11-19
[8] 张俊红, 汤周杰, 林杰威等. IVMD融合Robust ICA的内燃机噪声源分离 [J]. 振动、测试与诊断, 2020, 040(001): 28-34.
Zhang Junhong, Tang Zhoujie, Lin Jiewei, IVMD Fusion Robust ICA for Internal Combustion Engine Noise Source Separation [J] Vibration, Testing and Diagnosis, 2020, 040 (001): 28-34
[9] 张海龙, 宋业栋, 李鑫等. 基于优化 VMD 与欧氏距离的柴油机故障识别 [J]. 振动. 测试与诊断, 2020, 40(5): 911-1023.
Zhang Hailong, Song Yedong, Li Xin, Diesel engine fault identification based on optimized VMD and Euclidean distance [J] Vibration Testing and Diagnosis, 2020, 40 (5): 911-1023
[10] M. Nazari, S.M. Sakhaei, Variational mode extraction: a new efficient method to derive respiratory signals from ECG, IEEE J. Biomed. Health. Inf. 22 (2018)1059–1067.
[11] 俞惠惠，郑近德，潘海洋等，基于自适应变分模态提取的低速重载滚动轴承故障诊断方法[J]. 振动与冲击, 2022, 41(11): 65-71.
Yu Huihui, Zheng Jinde, Pan Haiyang, et al., Fault diagnosis method for low-speed heavy-duty rolling bearings based on adaptive variational mode extraction [J] Vibration and Shock, 2022, 41 (11): 65-71
[12] 郭远晶,金晓航,魏燕定,等.S变换引导变分模态提取的旋转机械故障诊断方法[J].振动工程学报,2022,35(05):1289-1298
Guo Yuanjing, Jin Xiaohang, Wei Yanding, Fault diagnosis method for rotating machinery using S-transformation guided variational mode extraction [J]. Journal of Vibration Engineering, 2022, 35 (05): 1289-1298
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(4792 KB)
