基于二维互补随机共振的轴承故障诊断方法研究

陆思良1,苏云升1,赵吉文1,何清波2,刘方1,刘永斌1

振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 7-12.

PDF(1726 KB)
PDF(1726 KB)
振动与冲击 ›› 2018, Vol. 37 ›› Issue (4) : 7-12.
论文

基于二维互补随机共振的轴承故障诊断方法研究

  • 陆思良1,苏云升1,赵吉文1,何清波2,刘方1,刘永斌1
作者信息 +

Bearings fault diagnosis based on two-dimensional complementary stochastic resonance

  • LU Si-liang1  SU Yun-sheng 1  ZHAO Ji-wen 1  HE Qing-bo 2  LIU Fang1  LIU Yong-bin1
Author information +
文章历史 +

摘要

一维随机共振(one-dimensional stochastic resonance, 1DSR)被广泛用于轴承故障诊断中。针对传统1DSR对微弱信号的检测效果不够理想,输出信号噪声大,不能准确获得轴承故障特征频率(fault characteristic frequency, FCF)等问题,本文提出一种新的二维互补随机共振(two-dimensional complementary stochastic resonance, 2DCSR)方法并应用于轴承故障诊断。首先将采集到的轴承故障信号根据共振带位置进行带通滤波并解调,随后将解调信号对半分成两个子信号并输入2DCSR的两个输入端,利用输出信号的加权功率谱峭度(WPSK)指标对2DCSR系统参数进行自适应调节优化,最终得到最优的滤波输出信号及频谱,以识别轴承FCF并诊断轴承故障类型。数值仿真及实验结果表明,本文提出的方法可以有效地增强轴承FCF并提高轴承故障诊断效果。

Abstract

One-dimensional stochastic resonance (1DSR) methods have been widely used in bearing fault diagnosis. However, some deficiencies still exist in the traditional 1DSR methods such as limited weak signal detection capacity, obvious output noise and inaccurate bearing fault characteristic frequency (FCF) for fault recognition, etc. To address these issues, this study proposes a new two-dimensional complementary stochastic resonance (2DCSR) method to enhance bearing fault diagnosis. First, the acquired bearing fault signal is bandpass-filtered according to the location of resonance band and then demodulated. Then the demodulated signal is split into two sub-signals and the sub-signals are sent to the two input channels of the 2DCSR. The weighted power spectral kurtosis (WPSK) of the output signal is used as the criterion to adaptively guide parameters tuning in the 2DCSR system. Finally the optimal output signal and its spectrum are obtained for bearing fault recognition. Numerical and experimental results show that the bearing FCF can be enhanced by the proposed 2DCSR method, thereby improving the performance of bearing fault diagnosis.
Key words: bearing fault diagnosis; two-dimensional complementary stochastic resonance; weighted power spectral kurtosis; weak signal detection

关键词

轴承故障诊断 / 二维互补随机共振 / 加权功率谱峭度 / 微弱信号检测

引用本文

导出引用
陆思良1,苏云升1,赵吉文1,何清波2,刘方1,刘永斌1. 基于二维互补随机共振的轴承故障诊断方法研究[J]. 振动与冲击, 2018, 37(4): 7-12
LU Si-liang1 SU Yun-sheng 1 ZHAO Ji-wen 1 HE Qing-bo 2 LIU Fang1 LIU Yong-bin1. Bearings fault diagnosis based on two-dimensional complementary stochastic resonance[J]. Journal of Vibration and Shock, 2018, 37(4): 7-12

参考文献

[1]项巍巍, 蔡改改, 樊薇, 等. 基于双调Q小波变换的瞬态成分提取及轴承故障诊断应用研究 [J]. 振动与冲击, 2015, 34 (10): 34-39.
XIANG Wei-wei, CAI Gai-gai, FAN Wei, et al., Transient feature extraction based on double-TQWT and its application in bearing fault diagnosis [J], Journal of vibration and shock, 2015, 34(10): 34-39.
[2]严如强, 钱宇宁, 胡世杰, 等. 基于小波域平稳子空间分析的风力发电机齿轮箱故障诊断 [J]. 机械工程学报, 2014, 50 (11): 9-16.
YAN Ruqiang, QIAN Yuning, HU Shijie, et al., Wind Turbine Gearbox Fault Diagnosis Based on Wavelet Domain Stationary Subspace Analysis [J], Journal of mechanical engineering, 2014, 50 (11): 9-16.
[3]Ruqiang Yan, Robert X Gao, Xuefeng Chen. Wavelets for fault diagnosis of rotary machines: A review with applications [J]. Signal Processing, 2014, 96: 1-15.
[4]郑近德, 程军圣, 杨宇. 改进的 EEMD 算法及其应用研究 [J]. 振动与冲击, 2013, 32 (21): 21-26+46.
ZHENG Jin-de, CHENG Jun-sheng, YANG Yu, Modified EEMD algorithm and its applications [J], Journal of vibration and shock, 2013, 32 (21): 21-26+46.
[5]Yaguo Lei, Jing Lin, Zhengjia He, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery [J]. Mechanical Systems and Signal Processing, 2013, 35 (1-2): 108-126.
[6]朱维娜, 林敏. 基于人工鱼群算法的轴承故障随机共振自适应检测方法 [J]. 振动与冲击, 2014, 33 (6): 143-147.
ZHU Wei-na,LIN Min, Method of adaptive stochastic resonance for bearing fault detection based on artificial fish swarm algorithm [J], Journal of vibration and shock, 2014, 33 (6): 143-147.
[7]冷永刚, 田祥友. 一阶线性系统随机共振在转子轴故障诊断中的应用研究 [J]. 振动与冲击, 2014, 33 (17): 1-5.
LENG Yong-gang, TIAN Xiang-you,  Application of a first-order linear system's stochastic resonance in fault diagnosis of rotor shaft [J], Journal of vibration and shock, 2014, 33 (17): 1-5.
[8]Leonardo Barbini, Matthew O. T. Cole, Andrew J. Hillis, et al. Weak signal detection based on two dimensional stochastic resonance [C]. IEEE 23rd European Signal Processing Conference (EUSIPCO), 2015: 2147-2151.
[9]Leonardo Barbini, Isabella Bordi, Klaus Fraedrich, et al. The stochastic resonance in a system of gradient type [J]. The European Physical Journal Plus, 2013, 128 (2): 1-12.
[10]Jun Wang, Qingbo He, Fanrang Kong. Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings [J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64 (2): 564 - 577.
[11]Siliang Lu, Qingbo He, Fanrang Kong. Effects of underdamped step-varying second-order stochastic resonance for weak signal detection [J]. Digital Signal Processing, 2015, 36 (2015): 93-103.
[12]CWRU. http://csegroups.case.edu/bearingdatacenter/pages/download-data-file.
[13]Siliang Lu, Qingbo He, Fanrang  Kong. Stochastic resonance with Woods–Saxon potential for rolling element bearing fault diagnosis [J]. Mechanical Systems and Signal Processing, 2014, 45 (2): 488-503.
[14]Siliang Lu, Qingbo He, Haibin Zhang, et al. Enhanced rotating machine fault diagnosis based on time-delayed feedback stochastic resonance [J]. Journal of Vibration and Acoustics-Transactions of the ASME, 2015, 137: 051008.
[15]Qingbo He, Jun Wang, Fei Hu, et al. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement [J]. Journal of Sound and Vibration, 2013, 332: 5635-5649.
 

PDF(1726 KB)

Accesses

Citation

Detail

段落导航
相关文章

/