基于IA-VMD的浮环密封声发射信号降噪与特征提取

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Abstract To address the problem that the acoustic emission signal was easily disturbed by external noise and the characteristic signal was difficult to be extracted during the operation of the aero-engine floating ring seal, an acoustic emission signal processing method based on Immune Algorithm (IA) and Variational mode decomposition (VMD) was proposed. Primarily the immune algorithm was applied to optimize the number of modes and penalty factors in the variational mode decomposition, and the sample entropy was used as the fitness function to obtain the best combination of parameters in the VMD algorithm. Moreover, the original signal was decomposed to obtain several modal components (Intrinsic mode function (IMF)) and the relative entropy of each component was calculated, and the components with small differences were selected for reconstruction to obtain the noise reduction signal. The simulation signal analysis shows that the IA-VMD method could obtain the best parameters and has obvious advantages in the anti-noise interference. Eventually, noise reduction and characterization of the acoustic emission signal of the floating ring seal show that the IA-VMD method could retain the maximum effective information while noise reduction, and obtain the acoustic emission signal characterizing the friction state of the main seal surface of the floating ring seal, laying the foundation for future fault diagnosis of the floating ring seal.

Key words： floating ring seal immune algorithm variational mode decomposition acoustic emission feature extraction

Received: 27 March 2023 Published: 28 February 2024

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	Articles by authors
	ZHANG Shuai1
	DING Junhua1
	DING Xuexing1
	LI Ning2

Cite this article:

ZHANG Shuai1,DING Junhua1,DING Xuexing1, et al. Noise reduction and feature extraction of acoustic emission signals from floating ring seals based on IA-VMD[J]. JOURNAL OF VIBRATION AND SHOCK, 2024, 43(4): 222-229.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2024/V43/I4/222

[1] Huang W, Lin Y, Liu Y, et al. Face Rub-Impact Monitoring of a Dry Gas Seal Using Acoustic Emission[J]. Tribology Letters, 2013, 52(2): 253-259. [2] Xu L, Liu Y, Wu J H, et al. Transient dynamic analysis and experimental verification on lubrication regime transition during startup period of non-contacting mechanical seal in liquid oxygen turbopump[J]. Tribology International, 2022, 176. [3] Hossein TOWSYFYAN,Fengshou GU,ANDrew D BALL, et al. Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements[J].Friction,2019,7(06):572-586. [4] 孙鑫晖，刘怀顺,王明洋,等.基于SVD-AVMD的液膜密封声发射特征提取[J].中国石油大学学报(自然科学版),2021,45(06):168-174. SUN Xinhui，LIU Huaishun, WANG Mingyang, et al. Acoustic emission feature extraction of liquid film seals based on SVD-AVMD [J]. Journal of China University of Petroleum (Natural Science Edition), 2021,45 (06): 168-174. [5] 林志斌,高宏力,吴昱东,等.基于EWT-KLD的机械密封金刚石涂层磨损声发射降噪[J/OL].西南交通大学学报:1-8. LIN Zhibin, GAO Hongli, WU Yudong, et al. Acoustic emission noise reduction of mechanical seal diamond coating wear based on EWT-KLD [J/OL]. Journal of Southwest Jiaotong University: 1-8. [6] Fan W J, Huang W F, Liu Y, et al. State Evolution of Dry Gas Seal during Repeated Start-Stop Operation Using Acoustic Emission Method[J]. Tribology Transactions, 2020, 63(1): 173-181. [7] Medina-Arenas M , Sopp F , Stolle J , et al. Measurement and Analysis of Inadequate Friction Mechanisms in Liquid-Buffered Mechanical Seals Utilizing Acoustic Emission Technique[J]. Vibration, 2021, 4(1):263-283. [8] 曹洁,张玉林,王进花,等.基于VMD和SVPSO-BP的滚动轴承故障诊断[J].太阳能学报,2022,43(09):294-301. CAO Jie, ZHANG Yulin, WANG Jinhua, et al. Rolling bearing fault diagnosis based on VMD and SVPSO-BP [J]. Journal of Solar Energy, 2022,43 (09): 294-301. [9] 张爽,王晓东,李祥,等.基于FVMD的滚动轴承故障特征提取方法[J].振动与冲击,2022,41(06):236-244. ZHANG Shuang, WANG Xiaodong, LI Xiang, et al. Fault feature extraction method of rolling bearing based on FVMD [J]. Vibration and Impact, 2022,41 (06): 236-244. [10] Kumar PS, Kumaraswamidhas LA, Laha SK. Selecting effective intrinsic mode functions of empirical mode decomposition and variational mode decomposition using dynamic time warping algorithm for rolling element bearing fault diagnosis. Transactions of the Institute of Measurement and Control. 2019;41(7):1923-1932. [11] 别锋锋,朱鸿飞,彭剑,等.基于VMD-MSE与SSA-SVM的往复式压缩机气阀故障诊断[J].振动与冲击,2022,41(19):289-295. BIE Fengfeng, ZHU Hongfei, PENG Jian, et al. Fault Diagnosis of Reciprocating Compressor Valve Based on VMD-MSE and SSA-SVM [J]. Vibration and Shock, 2022,41 (19): 289-295. [12] 张俊,张建群,钟敏,等.基于PSO-VMD-MCKD方法的风机轴承微弱故障诊断[J].振动.测试与诊断,2020,40(02):287-296+418. ZHANG Jun, ZHANG Jianqun, ZHONG Min, et al. Fan bearing weak fault diagnosis based on PSO-VMD-MCKD method [J]. Vibration. Test and Diagnosis, 2020, 40 (02): 287-296+418. [13] 陈祥,杨志强,田镇,等.GA-VMD与多尺度排列熵结合的GNSS坐标时序降噪方法[J/OL].武汉大学学报(信息科学版):1-16. CHEN Xiang, YANG Zhiqiang, TIAN Zhen, et al. GNSS coordinate time series noise reduction method combining GA-VMD and multi-scale permutation entropy [J/OL]. Journal of Wuhan University (Information Science Edition): 1-16. [14] 褚惟,王贵勇,刘韬,等.麻雀算法参数优化VMD联合K-SVD滚动轴承故障诊断[J].噪声与振动控制,2022,42(04):100-106. CHU Wei, WANG Guiyong, LIU Tao, et al. Sparrow algorithm parameter optimization VMD combined K-SVD rolling bearing fault diagnosis [J]. Noise and Vibration Control, 2022,42 (04): 100-106 [15] 王宏伟,孙文磊,张小栋,等.基于优化VMD复合多尺度散布熵及LSTM的风力发电机齿轮箱故障诊断方法研究[J].太阳能学报,2022,43(04):288-295. WANG Hongwei, SUN Wenlei, ZHANG Xiaodong, et al. Research on fault diagnosis method of wind turbine gearbox based on optimized VMD composite multi-scale dispersion entropy and LSTM [J]. Journal of Solar Energy, 2022,43 (04): 288-295. [16] 肖茂华,周爽,黄天逸,等.参数优化VMD-MPE和PSO-CS-Elman神经网络在滚动轴承故障诊断中的应用研究[J/OL].振动工程学报:1-14. XIAO Maohua, ZHOU Shuang, HUANG Tianyi, et al. Research on the application of parameter optimization VMD-MPE and PSO-CS Elman neural network in rolling bearing fault diagnosis [J/OL]. Journal of Vibration Engineering: 1-14. [17] 边杰,梅庆,陈亚农,等.CS-VMD与Teager包络谱在齿轮故障特征提取中的应用[J].浙江工业大学学报,2021,49(05):558-563. BIAN Jie, MEI Qing, CHEN Yanong, et al. Application of CS-VMD and Teager envelope spectrum in gear fault feature extraction [J]. Journal of Zhejiang University of Technology, 2021,49 (05): 558-563. [18] 张伟,李军霞,陈维望.基于蝙蝠算法优化VMD参数的滚动轴承复合故障分离方法[J].振动与冲击,2022,41(20):133-141. ZHANG Wei, LI Junxia, CHEN Weiwang. A composite fault separation method for rolling bearings based on bat algorithm to optimize VMD parameters [J]. Vibration and Shock, 2022,41 (20): 133-141. [19] Dragomiretskiy K, Zosso D. Variational Mode Decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531-544. [20] 梁尔祝,徐淼,谷传宝,等.基于SA-GA模糊熵的VMD算法在爆破振动信号分解中的应用[J].金属矿山,2022(02):75-82. LIANG Erzhu, XU Miao, GU Chuanbao, et al. Application of VMD algorithm based on SA-GA fuzzy entropy in blasting vibration signal decomposition [J]. Metal Mine, 2022 (02): 75-82. [21] Quwaider M , Shatnawi Y . Neural network model as Internet of Things congestion control using PID controller and immune-hill-climbing algorithm[J]. Simulation Modelling Practice and Theory, 2020，101：102022. [22] Zhu Yongbin ,Li Tao,Li Wenshan. An Efficient Hybrid Feature Selection Method Using the Artificial Immune Algorithm for High-Dimensional Data[J]. Computational Intelligence and Neuroscience, 2022, 2022 : 1452301-1452301. [23] 朱素霞,刘抒伦,孙广路.基于相对熵和K-means的形状相似差分隐私轨迹保护机制[J].通信学报,2021,42(02):113-123. ZHU Suxia, LIU Shulun, SUN Guanglu. Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means [J]. Journal of Communications, 2021,42 (02): 113-123. [24] Mitrakovic D , Grabec I , Sedmak S . Simulation of AE signals and signal analysis systems[J]. Ultrasonics, 1985, 23(5):227-232. [25] 力宁,江平,翁泽文,等.航空发动机浮环密封上浮性能试验研究[J].润滑与密封,2020,45(11):143-148. Li N, JIANG P, WENG Zewen, et al. Experimental study of floating ring seal uplift performance of aero-engine[J]. Lubrication and sealing,2020,45(11):143-148. [26] Towsyfyan H, Gu F, Ball A D, et al. Modelling acoustic emissions generated by tribological behaviour of mechanical seals for condition monitoring and fault detection[J]. Tribology International, 2018, 125: 46-58. [27] 李晓晖,傅攀,曹伟青,等.机械密封端面接触状态的声发射监测研究[J].振动与冲击,2016,35(08):83-89. LI Xiaohui, FU Pan, Cao Weiqing, et al. Research on acoustic emission monitoring of mechanical seal end face contact state [J]. Vibration and Impact, 2016, 35 (08): 83-89.