最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法

唐贵基,王晓龙

振动与冲击 ›› 2015, Vol. 34 ›› Issue (12) : 79-84.

PDF(1975 KB)
PDF(1975 KB)
振动与冲击 ›› 2015, Vol. 34 ›› Issue (12) : 79-84.
论文

最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法

  • 唐贵基,王晓龙
作者信息 +

Feature Extraction Method for Rolling Bearing Incipient Fault Based on Maximum Correlated Kurtosis Deconvolution and 1.5 Dimension Spectrum

  • Tang Guiji,Wang Xiaolong
Author information +
文章历史 +

摘要

滚动轴承处于早期故障阶段时,特征信号微弱,并且受环境噪声影响严重,因此故障特征提取困难。针对这一问题,本文尝试将最大相关峭度解卷积方法引入到滚动轴承故障诊断领域,并与1.5维谱结合,提出了最大相关峭度解卷积结合1.5维谱的轴承早期故障特征提取方法。首先对故障信号做最大相关峭度解卷积预处理,然后计算解卷积信号的包络信号,最后对包络信号做1.5维谱分析,从而得到解卷积信号的1.5维包络谱,通过分析谱图中幅值突出的频率成分来判断故障类型。滚动轴承故障模拟及实测信号分析结果表明,该方法可有效提取早期故障特征频率信息,具有一定可靠性。

Abstract

Early fault feature of rolling bearing is very weak and affected by environment noise seriously, so it is difficult to draw fault feature. Aiming at solving this problem, maximum correlated kurtosis deconvolution was introduced to the field of fault diagnosis for rolling bearing and combined with the 1.5 dimension spectrum, then feature extraction method for rolling bearing incipient fault based on maximum correlated kurtosis deconvolution and 1.5 dimension spectrum was proposed. Firstly, fault signal was processed by MCKD method and envelope signal of deconvolution signal was calculated, then analyzed the envelope signal using 1.5 dimension spectrum method and 1.5 dimension envelope spectrum of deconvolution signal was obtained, finally, bearing fault was judged by analyzing the frequency components of 1.5 dimension envelope spectrum. Analysis results of simulated and measured fault signal of rolling bearing showed that this method could effectively extract the feature frequency information of incipient fault and has a certain reliability.

关键词

滚动轴承 / 解卷积 / 1.5维谱 / 早期故障 / 特征提取

Key words

rolling bearing / deconvolution / 1.5 dimension spectrum / incipient fault / feature extraction

引用本文

导出引用
唐贵基,王晓龙. 最大相关峭度解卷积结合1.5维谱的滚动轴承早期故障特征提取方法[J]. 振动与冲击, 2015, 34(12): 79-84
Tang Guiji,Wang Xiaolong. Feature Extraction Method for Rolling Bearing Incipient Fault Based on Maximum Correlated Kurtosis Deconvolution and 1.5 Dimension Spectrum[J]. Journal of Vibration and Shock, 2015, 34(12): 79-84

参考文献

[1] 罗颂荣,程军圣,郑近德. 基于ITD分形模糊熵的轴承早期故障诊断[J].振动、测试与诊断,2013,33(4):706-711.
Luo Songrong,Cheng Junsheng,Zheng Jinde. Incipient Fault Diagnosis Based on ITD Fractal Dimension and Fuzzy Entropy for Bearings[J].Journal of Vibration,Measurement & Diagnosis,2013,33(4):706-711.
[2] 曾庆虎,邱静,刘冠军,等.基于小波相关滤波一包络分析的早期故障特征提取方法[J].仪器仪表学报,2008,29(4):729-933.
Zeng Qinghu,Qiu Jing,Liu Guanjun,et al. Approach to Extraction of Incipient Fault Features Based on Wavelet Correlation Filter and Envelope Analysis [J].Chinese Journal of Scientific Instrument,2008,29(4):729-933.
[3] 崔玲丽,康晨晖,胥永刚,等.滚动轴承早期冲击性故障特征提取的综合算法研究[J].仪器仪表学报,2010,31(11):2422-2427.
Cui Lingli,Kang Chenhui,Xu Yonggang,et al.Integrated Algorithm Research on Early Inpactive Fault Feature Extraction of Rolling Bearings[J].Chinese Journal of Scientific Instrument,2010,31(11):2422-2427.
[4] Jiang Ruilong,Chen Jin,Dong Guangming,et al. The Weak Fault Diagnosis and Condition Monitoring of Rolling Element Bearing Using Minimum Entropy Deconvolution and Envelop Spectrum[J].Engineering Science Engineers,Part C:Journal of Mechanical Engineering Science,2013,227(5):1116-1129.
[5] 苏文胜,王奉涛,张志新,等.EMD降噪和谱峭度法在滚动轴承早期故障诊断中的应用[J].振动与冲击,2010,29(3):18-21.
Su Wensheng,Wang Fengtao,Zhang Zhixin,et al. Application of EMD Denoising and Spectral Kurtosis in Early Diagnosis of Rolling Element Bearings[J].Journal of Vibration and Shock,2010,29(3):18-21.
[6] Mcdonald G L,Zhao Q,Zuo M J. Maximum Correlated Kurtosis Deconvolution and Application on Gear Tooth Chip Fault Detection[J].Mechanical Systems and Signal Processing,2012,33:237-255.
[7] 唐贵基,王晓龙.基于局部均值分解和切片双谱的滚动轴承故障诊断研究[J].振动与冲击,2013,32(24):83-88.
Tang Guiji,Wang Xiaolong. Fault Diagnosis of Roller Bearings Based on Local Mean Decomposition and Slice Bispectrum[J].Journal of Vibration and Shock,2013,32(24):83-88.
[8] 陈略,紫艳阳,何正嘉,等.噪声协助的EMD-1.5维谱信号抗混分解与特征提取[J].振动与冲击,2010,29(5):26-30.
Chen Lue,Zi Yanyang,He Zhengjia,et al. Noise-assisted EMD-1.5 Dimension Spectrum for Signal Anti-alias Decomposition and Feature Extraction[J].Journal of Vibration and Shock,2010,29(5):26-30.
[9] Antoni J,Bonnardot F,Raad A. Cyclostationary Modeling of Rotating Machine Vibration Signals[J].Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[10]Randall R B,Antoni J,Chobsaard S.The Relationship Between Spectral Correlation and Envelope Analysis in the Diagnosis of Bearing Faults and Other Cyclostationary Machine Signals[J].Mechanical Systems and Signal Processing,2001,15(5):945-962.

PDF(1975 KB)

775

Accesses

0

Citation

Detail

段落导航
相关文章

/