势函数特征参数调节的随机共振及其动车轴承故障检测研究

刘进军1,2,冷永刚1,张雨阳1,谭丹1,范胜波1

振动与冲击 ›› 2019, Vol. 38 ›› Issue (13) : 26-33.

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PDF(1244 KB)
振动与冲击 ›› 2019, Vol. 38 ›› Issue (13) : 26-33.
论文

势函数特征参数调节的随机共振及其动车轴承故障检测研究

  • 刘进军1,2,冷永刚1,张雨阳1,谭丹1,范胜波1
作者信息 +

Stochastic resonance with adjustable potential function characteristic parameters and its application in EMU bearing fault detection

  • LIU Jinjun1,2, LENG Yonggang1, ZHANG Yuyang1, TAN Dan1, FAN Shengbo1
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文章历史 +

摘要

双稳随机共振的系统参数调节,会使势函数的势垒高度和势阱间距同时发生变化。为了能直观了解势函数特征的变化对双稳随机共振的影响,本文通过变量代换法实现势函数特征参数(势垒高度参数和势阱间距参数)的解耦,有利于势函数的调节。并提出了基于势函数特征参数调节的随机共振方法,根据Kramers逃逸速率与目标信号频率匹配原则,实现了目标信号的检测。为了克服随机共振对采样频比的要求,文中将势函数特征参数调节的随机共振与频域信息交换技术相结合,实现了低采样频比信号的检测。所提方法应用于动车转向架轴承故障的检测结果验证了方法的有效性。

Abstract

Potential function characteristic parameters (PFCPs) including barrier height and potential well spacing are changed simultaneously when system parameters are adjusted in a classical bi-stable stochastic resonance (SR) system. Here, the variable substitution method was used to realize PFCPs being decoupled, which was beneficial to adjusting potential function. Then the SR method based on adjustable PFCPs was proposed, according to the matching principle between Kramers escape rate and target signal frequency, target signal detection was realized. In order to overcome the difficulty of sampling frequency ratio’s high demand for SR, the technique of frequency information exchange (FIE) was introduced and combined with the proposed method to realize signal detection with lower sampling frequency ratio. The detection results for bearing faults of EMU bogie system verified the effectiveness of the proposed method.

关键词

势函数特征参数 / 随机共振 / 信号检测 / 动车轴承

Key words

 potential function characteristic parameters / SR / signal detection / high-speed railway bearing

引用本文

导出引用
刘进军1,2,冷永刚1,张雨阳1,谭丹1,范胜波1. 势函数特征参数调节的随机共振及其动车轴承故障检测研究[J]. 振动与冲击, 2019, 38(13): 26-33
LIU Jinjun1,2, LENG Yonggang1, ZHANG Yuyang1, TAN Dan1, FAN Shengbo1. Stochastic resonance with adjustable potential function characteristic parameters and its application in EMU bearing fault detection[J]. Journal of Vibration and Shock, 2019, 38(13): 26-33

参考文献

[1] 于德介,成  琼,程军圣. 基于复解析小波变换的瞬时频率分析方法[J]. 振动与冲击, 2004,23(1): 108-109.
Yu De-jie, Cheng Qiong, Cheng Jun-sheng. Transient frequency analysis based on complex analytical wavelet transform and its application to fault dignosis in gear drive [J]. Journal of vibration and shock, 2004,23(1): 108-109.
[2] Peter W T, Peng Y H, Yam R. Wavelet analysis and envelope detection for rolling element bearing fault diagnosis—their effectiveness and flexibilities [J]. Journal of vibration and acoustics, 2001, 123(3): 303-310.
[3] 时培明,丁雪娟,李  庚,韩东颖. 一种EMD改进方法及其在旋转机械故障诊断中的应用[J]. 振动与冲击,2013,32(4): 185-190.
Shi Pei-ming, Ding Xue-juan, Li Geng, Han Dong-ying. An improved method of EMD and its applications in rotating machinery fault diagnosis[J]. Journal of vibration and shock, 2013,32(4): 185-190.
[4] 胡爱军,向  玲,唐贵基,杜永祚. 基于数学形态变换的转子故障特征提取方法[J]. 机械工程学报, 2011,47(23): 92-96.
Hu Ai-jun, Xiang Ling, Tang Gui-ji, Du Yong-zuo. Fault feature extracting method of rotating machinery based on mathematical morphology [J]. Journal of mechanical engineering, 2011,47(23): 92-96.
[5] Lai Z H, Leng Y G. Generalized parameter-adjusted stochastic resonance of duffing oscillator and its application to weak-signal detection[J]. Sensors, 2015,15(9): 21327-21349.
[6] Leng Y G, Leng Y S, Wang T Y, et al. Numerical analysis and engineering application of large parameter stochastic resonance[J]. Journal of Sound and Vibration, 2006, 292(3): 788-801.
[7] 林  敏,黄咏梅.基于调制随机共振的转子故障早期检测[J]. 中国电机工程学报,2006,26(8): 128-131.
Lin Min, Huang Yong-mei. Incipient fault detection for rotor system based on modulated stochastic resonance [J]. Proceedings of CSEE, 2006,26(8): 128-131.
[8] 陈  敏,胡茑庆,秦国军,安茂春. 参数调节随机共振在机械系统早期故障检测中的应用[J]. 机械工程学报,2009 ,45 (4) : 131-135.
Chen Min,Hu Niao-qing, Qin Guo-jun, An Mao-chun. Application of parameter-tuning stochastic resonance for detecting early mechanical faults[J]. Journal of mechanical engineering, 2009,45(4): 131-135.
[9] 杨定新,胡  政,杨拥民. 大参数周期信号随机共振解析[J]. 物理学报,2012,61(8): 080501.
Yang Ding-xin, Hu Zheng, Yang Yong-min. The analysis of stochastic resonance of periodic signal with large parameters [J]. Acta Physica Sinica,2012,61(8): 080501.
[10] He Q, Wang J, Liu Y, et al. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines[J]. Mechanical Systems and Signal Processing, 2012, 28: 443-457.
[11] Shi P, Ding X, Han D. Study on multi-frequency weak signal detection method based on stochastic resonance tuning by multi-scale noise[J]. Measurement, 2014, 47: 540-546.
[12] 刘进军,冷永刚,赖志慧,谭  丹. 基于频域信息交换的随机共振研究[J]. 物理学报, 2016,65(22): 220501.
Liu Jin-jun, Leng Yong-gang, Lai Zhi-hui, Tan Dan. Stochastic resonance based on frequency information exchange [J]. Acta Physica Sinica,2016,65(22): 220501.
[13] Liu X, Liu H, Yang J, et al. Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system[J]. Mechanical Systems and Signal Processing, 2017, 96: 58-76.
[14] Gammaitoni L, Hänggi P, Jung P, et al. Stochastic resonance[J]. Reviews of modern physics, 1998, 70(1):223.

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