局部特征尺度与小波阈值协同下密封摩擦面AE信号降噪研究

陆俊杰1,刘柱1,2,丁雪兴2,丁俊华2,高德1

振动与冲击 ›› 2023, Vol. 42 ›› Issue (12) : 205-211.

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (12) : 205-211.
论文

局部特征尺度与小波阈值协同下密封摩擦面AE信号降噪研究

  • 陆俊杰1,刘柱1,2,丁雪兴2,丁俊华2,高德1
作者信息 +

A study on noise reduction of AE signal of sealing friction surface under the synergy of local characteristic-scale decomposition and wavelet threshold

  • LU Junjie1,LIU Zhu1,2,DING Xuexing2,DING Junhua2,GAO De1
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文章历史 +

摘要

针对机械密封低速阶段存在信号内源耦合与外部干扰因素的共性问题,提出了信号局部特征尺度分解(Local Characteristic-scale Decomposition)与小波阈值协同降噪方法。基于局部特征尺度分解结合小波阈值方法与声发射信号降噪的信息映射关系,建立了互相关系数对含噪分量识别机制,将纯净分量和降噪后的含噪分量进行重构,实现信号降噪,并搭建了机械密封声发射测试台,试验结果表明:LCD-新阈值降噪方法降噪效果优于LCD强制降噪和小波阈值降噪,LCD-新阈值降噪的信噪比分别比LCD强制降噪和小波阈值降噪高出20%和23%。从而证明基于局部特征尺度下密封声发射信号协同小波阈值的降噪技术维持了信号的可用性,保障信号故障特征,为进行机械密封全生命周期管理奠定了理论基础。

Abstract

According to the common problem of internal signal coupling and external interference factors a synergistic noise reduction method is proposed, which based on local characteristic-scale decomposition (Local Characteristic-scale Decomposition) and wavelet threshold in the low-speed stage of mechanical seals. Based on the information mapping relationship between local feature scale decomposition combined with wavelet threshold method and acoustic emission signal noise reduction, a cross-correlation coefficient, which is identification mechanism for noisy components, is established, and the pure components and the denoised noise components are reconstructed to achieve signal reduction. noise, and built a mechanical seal acoustic emission test bench. The test results show that the noise reduction effect of LCD-new threshold noise reduction method is better than LCD forced noise reduction and wavelet threshold noise reduction, and the signal-to-noise ratio of LCD-new threshold noise reduction is higher than LCD forced noise reduction and wavelet threshold noise reduction are 20% and 23% higher. Therefore, it is proved that the noise reduction technology based on the acoustic emission signal of the seal in conjunction with the wavelet threshold at the local characteristic scale maintains the availability of the signal, guarantees the fault characteristics of the signal, and lays a theoretical foundation for the full life cycle management of the mechanical seal.

关键词

机械密封 / 启停 / 信号降噪 / 声发射 / LCD -小波阈值

Key words

Mechanical seal / Start and stop / Signal noise reduction / sound emission / LCD-Wavelet threshol

引用本文

导出引用
陆俊杰1,刘柱1,2,丁雪兴2,丁俊华2,高德1. 局部特征尺度与小波阈值协同下密封摩擦面AE信号降噪研究[J]. 振动与冲击, 2023, 42(12): 205-211
LU Junjie1,LIU Zhu1,2,DING Xuexing2,DING Junhua2,GAO De1. A study on noise reduction of AE signal of sealing friction surface under the synergy of local characteristic-scale decomposition and wavelet threshold[J]. Journal of Vibration and Shock, 2023, 42(12): 205-211

参考文献

[1]  Huang W ,  Lin Y ,  Gao Z , et al. An Acoustic Emission Study on the Starting and Stopping Processes of a Dry Gas Seal for Pumps[J]. Tribology Letters, 2013, 49(2):379-384.
[2]  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.
[3]  高志,林尤滨,黄伟峰,等. 干气密封启动过程中的声发射信号特征[J].清华大学学报: 自然科学版,2013,53( 3) :319 - 322.
GAO Zhi,LIN Youbin,HUANG Weifeng,et al. Acoustic emission characteristics of dry gas seals during startup[J].Journal of Tsinghua University: Science and Technology,2013,53( 3) : 319 - 322.
[4]  张尔卿. 机械密封端面状态监测及寿命预测关键技术研究[D]. 成都:西南交通大学,2015.
ZHANG Erqing. Research of key technologies on mechanical seal end face condition monitoring and life prediction [D]. Chengdu:Southwest Jiaotong University,2015.
[5]  李克斯,张尔卿,傅攀,等. 不完备先验知识下的机械密封端面磨损状态评估研究[J]. 摩擦学学报,2016,36(6):717-725.
LI Kesi,ZHANG Erqing,FU Pan,et al. Condition assessment on mechanical seal face wear based on incomplete prior knowledge [J]. Tribology,2016,36(6):717-725.
[6]  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(8):83-89.
LI Xiaohui,FU Pan,CAO Weiqing,et al.The study of acoustic emission monitoring for contact state of seal end faces[J].Journal of Vibration and Shock,2016,35( 8) : 83-8
[7]  Li X ,  Pan F ,  Kan C , et al. The Contact State Monitoring for Seal End Faces Based on Acoustic Emission Detection[J]. Shock and Vibration,2016,(2015-12-29), 2015, 2016:1-8.
[8]  Yin Y ,  Liu X ,  Huang W , et al. Gas face seal status estimation based on acoustic emission monitoring and support vector machine regression[J]. Advances in Mechanical Engineering, 2020, 12(5):168781402092132.
[9]  Luo Y ,  Zhang W ,  Fan Y , et al. Analysis of Vibration Characteristics of Centrifugal Pump Mechanical Seal under Wear and Damage Degree[J]. Shock and Vibration, 2021, 2021(2):1-9.
[10]  陈金林,丁雪兴,唐莉萍. 干气密封环磨合过程摩擦振动信号混沌特性分析[J]. 工程科学与技术,2021,53(03):205-214.
CHEN Jinlin,DING Xuexing,TANG Liping. Chaotic characteristics analysis of friction vibration signal in dry gas sealing ring running-in process[J]. Engineering Science and Technology,2021,53(03):205-214.
[11]  尹源,黄伟峰,刘向锋,刘莹,王玉明,李鲲. 机械密封智能化的技术基础和发展趋势[J]. 机械工程学报,2021,57(03):116-128.
YIN Yuan, HUANG Weifeng, LIU Xiangfeng, et al. Technical Basis and Development Trend of Mechanical Seal Intelligence [J]. Journal of Mechanical Engineering. 2021, 57(3): 116-128.
[12]  Boudraa A O ,  Cexus J C ,  Saidi Z . EMD-Based Signal Noise Reduction[J]. Signal Processing, 2005, 1(1).
[13]  刘东瀛,邓艾东,刘振元,李晶,张瑞,黄宏伟. 基于EMD与相关系数原理的故障声发射信号降噪研究[J]. 振动与冲击,2017,36(19):71-77.
LIU Dongyin,DENG Aidong,LIU Zhenyuan,et al. De-noising method for fault acoustic emission signals based on the EMD and correlation coefficient[J]. Journal of Vibration and Shock,2017,36 ( 19) : 71-77.
[14]  费鸿禄,刘梦,曲广建,高英. 基于集合经验模态分解-小波阈值方法的爆破振动信号降噪方法[J]. 爆炸与冲击,2018,38(01):112-118.
FEI Honglu,LIU Meng,QU Guangjian,et al. Denoising method of blasting vibration signal based on set empirical mode decomposition-wavelet threshold method[J]. Explosion and Shock Waves,2018,38 ( 1) : 112-118. ( in Chinese)
[15]  Chang J ,  Zhu L ,  Li H , et al. Noise reduction in Lidar signal using correlation-based EMD combined with soft thresholding and roughness penalty[J]. Optics Communications, 2018, 407:290-295.
[16]  Zhang L ,  Chang J ,  Li H , et al. Noise Reduction of LiDAR Signal via Local Mean Decomposition Combined with Improved Thresholding Method[J]. IEEE Access, 2020, PP(99):1-1.
[17]  Vargas R N ,  Veiga A P . Seismic trace noise reduction by wavelets and double threshold estimation[J]. Iet Signal Processing, 2018, 11(9):1069-1075.
[18]  Wang H ,  Wang S ,  Wang X , et al. RDTS noise reduction: A fast method study based on signal waveform type[J]. Optical Fiber Technology, 2021, 65(1):102594.
[19]  X  Ma,  Shi T ,  Xu H , et al. Noise Reduction for Ground-based Atmospheric Detection Lidar: A Universal Method Based on Signal Segmentation and Reconstruction[J]. Journal of Quantitative Spectroscopy and Radiative Transfer, 2021(20):107766.
[20]  陈是扦,彭志科,周鹏. 信号分解及其在机械故障诊断中的应用研究综述[J]. 机械工程学报,2020,56(17):91-107.
Chen Shiqian,Peng Zhike,Zhou Peng. Review of signal decomposition theory and its applications in machine fault diagnosis[J].Journal of Mechanical Engineering,2020,56( 17) : 91-107.
[21]  王海龙,赵岩,王海军,彭婵媛,仝潇. 基于CEEMDAN-小波包分析的隧道爆破信号去噪方法[J]. 爆炸与冲击,2021,41(05):125-137.
WANG Hailong,ZHAO Yan,WANG Haijun,et al. De-noising method of tunnel blasting signal based on CEEMDAN decomposition-wavelet packet analysis[J]. Explosion and Shock , 2021,41(05):125-137.
[22]  Donoho D L . De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3):P.613-627.
[23]  Borras D ,  Castilla M ,  Moreno N , et al. Wavelet and neural structure: a new tool for diagnostic of power system disturbances[J]. IEEE Transactions on Industry Applications, 2001, 37(1):184-190.
[24]  Donoho D L . Apapting to unknown smoothness via Kavelet shrinkage[J]. J American Statistical Association, 1995, 90.
[25]  王普,李天垚,高学金,高慧慧. 分层自适应小波阈值轴承故障信号降噪方法[J]. 振动工程学报,2019,32(03):548-556.
WANG Pu ,LI Tianyao,GAO Xuejin ,et al. Bearing Fault Signal Denoising Method of Hierarchical Adaptive Wavelet Threshold Function[J]. Journal of Vibration Engineering,32( 3) : 548.

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