变核函数的WVD在汽车轴承故障检测中的应用
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摘要
由于轴承各个阶段的故障信号存在一定差异,且固定核函数的维格纳分布(WVD)无法适应不同类型的信号,为此,提出利用可变核函数平滑伪WVD对汽车轴承故障进行检测。建立WVD的特征函数,利用特征函数的聚散性抑制交叉项干扰;在此基础上,将不同类型信号核函数的确定转化为最优化化问题,并给出核形状与核参数协同优化的关系式。采用变核平滑方法的WVD对汽车轴承故障进行检测,结果表明:变核函数的平滑WVD对信号的适应性更强,且能有效抑制交叉项振荡,在计算成本略有增加的基础上,检测准确率达到了97%以上。
Because the bearing fault signal in various stages had some differences, and Wigner-Ville distribution with fixed kernel function could not adapt to different types of signals, pseudo WVD smoothened by variable kernel function was proposed to detect vehicle bearing fault. The characteristics function of the WVD was established and its cross-term interference was restrained by using the convergence of the characteristic function.On the basis,determination of different kernel functions was converted to optimization problems. Then the relation of collaborative optimization of kernel function shape and parameters were given.WVD with variable kernel was adopted to detect vehicle bearing fault.The results show that WVD with variable kernel has a stronger adaptability to signals and it can effectively restrain the oscillation of cross terms. The detection accuracy can reached more than 97% with micro growth of the calculation cost.
Because the bearing fault signal in various stages had some differences, and Wigner-Ville distribution with fixed kernel function could not adapt to different types of signals, pseudo WVD smoothened by variable kernel function was proposed to detect vehicle bearing fault. The characteristics function of the WVD was established and its cross-term interference was restrained by using the convergence of the characteristic function.On the basis,determination of different kernel functions was converted to optimization problems. Then the relation of collaborative optimization of kernel function shape and parameters were given.WVD with variable kernel was adopted to detect vehicle bearing fault.The results show that WVD with variable kernel has a stronger adaptability to signals and it can effectively restrain the oscillation of cross terms. The detection accuracy can reached more than 97% with micro growth of the calculation cost.
引文
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[2] KAKARALA R,OGUNBONA P O.Signal Analysis Using a Multiresolution Form of the Singular Value Decomposition[J].IEEE Transactions on Image Processing,2001,10(5):724-735.
[3] 张超,陈建军,郭迅.基于EMD能量熵和支持向量机的齿轮故障诊断方法[J].振动与冲击,2010,29(10):216-220.ZHANG C,CHEN J J,GUO X.A Gear Fault Diagnosis Method Based on EMD Energy Entropy and SVM[J].Journal of Vibration and Shock,2010,29(10):216-220.
[4] 赵志宏,杨绍普.于双树复小波包变换和SVM的滚动轴承故障诊断方法[J].振动与冲击,2012,31(6):136-140.ZHAO Z H,YANG S P.Sample Entropy-based Roller Bearing Fault Diagnosis Method[J].Journal of Vibration and Shock,2012,31(6):136-140.
[5] 陆爽,田野.滚动轴承微弱故障信号检测的研究[J].组合机床与自动化加工技术,2004(8):3-5.LU S,TIAN Y.Research on Measurement of Rolling Bearing Faint Vibration Signal[J].Modular Machine Tool & Automatic Manufacturing Technique,2004(8):3-5.
[6] 关红彦,肖伟,吕扬生.抑制维格纳分布交叉项干扰的自谱窗方法[J].信号处理,2003,19(3):215-218.GUAN H Y,XIAO W,LV Y S.The Self-spectrum Window(SSW)Method of WVD’s Interference-term Suppression[J].Signal Processing,2003,19(3):215-218.
[7] 陈春俊,肖衡.维格纳-威利分布算法研究[J].西南交通大学报,2002,37(2):146-149.CHEN C J,XIAO H.Research and Application of Wigner-Ville Distribution Algorithm[J].Journal of Southwest Jiaotong University,2002,37(2):146-149.
[8] 程发斌,唐宝平,刘文艺.一种抑制维格纳分布交叉项的方法及在故障诊断中应用[J].中国机械工程,2008,19(14):1727-1731.CHENG F B,TANG B P,LIU W Y.A Method to Suppress Cross-term of Wigner-Ville Distribution and Its Application in Fault Diagnosis[J].China Mechanical Engineering,2008,19(14):1727-1731.