提出了一种分数阶傅里叶变换(Fractional Fourier Transform, FRFT)循频滤波方法，贴近瞬变工况下信号频率曲线变化特征，循迹剥离包含故障信息的特征分量，提取齿轮早期故障微弱特征。首先，研究了线性多尺度分段方法，将频率呈曲线任意变化信号自适应分成若干个频率近线性变化的信号段；然后研究了频率拟合确定FRFT滤波参数的方法，计算各段信号的FRFT滤波参数并逐段进行FRFT滤波，实现FRFT循频滤波。采用该方法对变速器加减速过程振动信号进行滤波解调分析，试验结果表明：线性多尺度分段方法，能自适应地将任一频率呈曲线任意变化信号分段成若干个频率近线性变化的信号段，且分段数较少；频率拟合确定FRFT滤波参数方法，不受振源和多分量数量影响，能准确确定各分段信号的FRFT滤波参数；该滤波方法能从变速器瞬变工况振动信号中循频提取出包含故障信息的特征分量，有效剥离其他分量和噪声干扰，对提取后的特征分量进行解调分析，能准确提取出传统方法难以识别的齿轮早期故障微弱特征。
Abstract
To extract the weak fault feature of gear, aFractional Fourier Transform(FRFT) filter method based on frequency tracking (FFFT) is proposed to peel the feature components that including fault information by tracking frequency and closely to curve feature of frequency under transient condition. Firstly, the linear multi-scale segmentation method is researched to divide the curve frequency signal into some segments whose frequency are similar to linear; Then, the method to ascertain the filtering parameters by matching the frequency is researched to calculate the FRFT filtering parameters of each segmental signal, and the signal in every segmentation is filtered by FRFT filter using these parameters to complete the FFFT method. The vibration signal of gearbox’s acceleration and deceleration process is analyzed by FFFT and the filtered signal is demodulated, the experimental results show that : linear multi-scale segmentation method is able to divide any curve frequency signal into almost least segments whose frequency are similar to linear; the method to ascertain the filtering parameters by matching the frequency is able to accurately ascertain the FRFT filter parameters of each segmental signals without the influence of the vibration source and the quantity of multi-components; the feature component including fault information of gearbox’s transient condition is accurately extracted by this filter method, the other components and noise are peeled at the same time. The early fault of gear is exactly extracted by the demodulation of extracted feature component, which is difficult to be identified by the traditional method.
关键词
FRFT /
循频滤波 /
微弱故障 /
特征提取
{{custom_keyword}} /
Key words
FRFT /
Frequency tracking filter;Weak fault /
Feature Extraction
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] Vishwash B, Dr. SrinivasaPai P, Dr.N.S.Sriram, et al..Multiscale slope feature extraction for gear and bearing fault diagnosis using wavelet transform[J]. Procedia Materials Science ,2014,5: 1650-1659.
[2] XU You-cai, LI Xin-Shi, TAO Ran, et al.. The application of local mean decomposition and variable predictive model-based class discriminate in gear fault diagnosis[J]. Advanced Materials Research, 2014, 1014: 510-515.
[3] CUILing-li, MODai-yi, WUNa. Application of sparse signal decomposition using dual-BP in gear-box weak fault diagnosis[J]. Yi Qi Yi Biao XueBao,2014, 35: 2633-2640.
[4] TIANHao, KANGXiao-yong, LIYong-jian. Fault diagnosis of gear wearing based on order cepstrumanalysis[J]. Applied Mechanics and Materials,2014, 543: 922-925.
[5] 彭富强，于德介等. 基于稀疏信号分解的自适应时变滤波器及其在齿轮故障诊断中的应用[J]. 机械工程学报，2010,46(24):46-53.
PENG Fu-qiang, YU De-jie, LUOJie-si, et al. MSCSSD-based Adaptive Time-varying Filter and Its Application to Gearbox Fault Diagnosis [J]. Journal of Mechanical Engineering, 2010,46(24):46-53.
[6] 彭富强. 多尺度线调频基稀疏信号分解及其在齿轮箱故障诊断中的应用[D]. 长沙: 湖南大学，2010.
PENG Fu-qiang. Research on Multi-scale Chirplet Based Sparse Signal Decomposition And Its Application In the Fault Diagnosis of Gearboxes [D].Chang sha: Hunan University, 2010.
[7] 陈小龙，关键, 刘宁波，等. 基于FRFT的LFM信号自适应滤波算法及分析[J]. 现代雷达，2010,32(12):48-53;
CHEN Xiao-long, GUAN Jian, LIUNing-bo, et al..Adaptive Filtering Algorithm for LFM Signal and Performance Analysis Based on FRFT [J].Modern Radar, 2010,32(12):48-53;
[8] 齐林，陶然. 基于分数阶傅里叶变换的线性调频信号的自适应时频滤波[J]. 兵工学报，2003,24(4):499-502.
QIN Lin, TAO Ran. An Adaptive Time-Frequency Filtering Method Based On Fractional Fourier Transform For Linear Frequency Modulation Signals [J]. Acta Armamentarii, 2003, 24(4): 499-502.
[9] 陶然，邓兵，王越. 分数阶傅里叶变化及其应用[M]. 北京：清华大学出版社, 2009.
TAORan, DENGBing, WANG Yue. Fractional Fourier Transform and Its Applications [M]. Beijing: Tsinghua University Press, 2009.
[10] 梅检民，肖云魁，沈虹，等. 多阶FRFT自适应滤波及齿轮微弱故障特征提取[J]. 振动工程学报,2013,26(5):771-778.
MEI Jian-min, XIAOYun-kui, SHEN Hong,et al..Multi-order FRFT adaptive filter and its application to extract weak fault feature based on sparse signal decomposition[J]. Journal of Vibration Engineering, 2013,26(5):771-778.
[11] 李枫，梅检民，肖云魁，等. 基于转速自适应多阶FRFT滤波提取瞬变工况齿轮微弱故障特征[J]. 振动与冲击, 2013,32(21):158-163.
LI Feng, MEI Jian-min, XIAO Yun-kui, et al.. Extraction of gearbox fault features under transient-conditions based on self-adaptive segmentation and multilevel fractional fourier transformation filtering [J],Journal of Vibration and Shock, 2013,32(21):158-163.
[12] 罗洁思，于德介，陈向民. 线调频小波路径追踪算法和FRFT相结合的升降速齿轮故障诊断方法[J]. 机械工程学报，2012, 48(7):56-61.
LUOJie-si, YU De-jie, CHEN Xiang-min, et al.. Gear Fault Diagnosis under the Speed-up and Speed-down Process Based on the Chirplet Path Pursuit Algorithm and FrFT [J],Journal OF Mechanical Engineering, 2012, 48(7):56-61.
[13] 刘峰，徐会法. 分数阶Fourier变换中量纲归一化因子的选取[J].系统工程与电子技术，2011,33(2):237-240.
LIU Feng, XUHui-fa. Selection of dimensional normalization parameters in fractional Fourier transform [J]. Systems Engineering and Electronics, 2011, 33(2): 237-240.
[14] 臧顺全，王竹霞. 基于分数傅里叶变换的信号检测方法[J].科学技术与工程，2010，10(3)：651-655.
ZANG Shun-quan, WANG Zhu-xia. Signal Detection Method Based on Fractional Fourier Transform [J]. Science Technology and Engineering, 2010, 10(3): 651-655.
[15] 赵兴浩. 分数阶傅里叶变换数值计算中的量纲归一化[J].北京理工大学学报，2005，25(4)：361：364.
ZHAO Xing-hao. Dimensional Normalization in the Digital Computation of the Fractional Fourier Transform[J]. Journal of Beijing Institute of Technology, 2005, 25(4): 361:364.
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}
PDF(2025 KB)
