Application of the Volterra Kernel Function Method in Feature Extraction of Bearing Ball Wear
-
摘要: 针对滚动轴承滚珠磨损故障特征难以提取的问题,提出一种基于多脉冲激励法下的Volterra级数核的求解算法.该方法是一种非线性系统模型的“交叉”诊断法,利用轴承系统输入输出的采样信号,建立Volterra非线性辨识系统模型,并运用多脉冲激励Volterra低阶核求解算法,将得到的低阶核通过时域和频域进行对比来判断轴承当前所处的运行状态.该文以无心车床主轴轴承为例进行实验验证,并与传统的小波分析法对比得出:多脉冲激励法能够方便准确地提取轴承的故障特征,该方法对此类故障的诊断具有一定的借鉴意义.Abstract: The fault features are difficult to be extracted from the worn rolling ball bearings. To tackle this problem, an algorithm of the Volterra series kernel based on the multiple-pulse excitation method was proposed. This method belongs to the cross diagnosis for nonlinear system models, which utilizes the sampled signal input and output of the bearing system to establish the Volterra nonlinear identification system model and applies the Volterra low-order kernel algorithm based on the multiple-pulse excitation method to obtain the low-order kernel, then the low-order kernel will be compared in aspects of the GIRF and the GFRF to estimate the present running state of the bearing system. The major bearing of a centerless lathe was taken for example to verify this method through experiment. In contrast to the traditional wavelet analysis method, the multiple-pulse excitation method helps extract the fault features of the ball bearing conveniently and exactly. Thus, the proposed method has much significance to the diagnosis of such faults.
-
[1] 李雅梅, 陈明霞, 杜晶. 小波理论在滚动轴承故障诊断中的应用[J]. 计算机系统应用, 2012,21(7): 172-176.(LI Ya-mei, CHEN Ming-xia, DU Jing. Application of wavelet theory to fault diagnosis of rolling bearing[J]. Computer Systems & Applications,2012,21(7): 172-176.(in Chinese)) [2] 崔宝珍, 潘宏侠. 小波分析在滚动轴承故障诊断中的应用[J]. 科技情报开发与经济, 2005,15(2): 176-178.(CUI Bao-zhen, PAN Hong-xia. The application of the wavelet analysis in the fault diagnosis of rolling bearings[J]. Sci-Tech Information Development & Economy,2005,15(2): 176-178.(in Chinese)) [3] 赵勇, 宗智, 王天霖. 一种抑制激波计算中数值振荡现象的双重小波收缩方法[J]. 应用数学和力学, 2014,35(6): 620-629.(ZHAO Yong, ZONG Zhi, WANG Tian-lin. A dual wavelet shrinkage procedure for supprssing numerical oscillation in shock wave calculation[J]. Applied Mathematics and Mechanics,2014,35(6): 620-629.(in Chinese)) [4] 林英祥, 孙清磊, 陈萍, 等. 冲击脉冲技术在滚动轴承故障诊断中的应用[J]. 海军工程大学学报, 2013,25(4): 85-90, 107.(LIN Ying-xiang, SUN Qing-lei, CHEN Ping, et al. Application of impact pulse technology to roller bearing failure detection[J]. Journal of Naval University of Engineering, 2013,25(4): 85-90, 107.(in Chinese)) [5] 冷军发, 荆双喜, 华伟. EMD与同态滤波解调在滚动轴承故障诊断中的应用[J]. 河南理工大学学报(自然科学版), 2014,33(5): 611-615.(LENG Jun-fa, JIN Shuang-xi, HUA Wei. Application of EMD and homomorphic filtering demodulation to fault diagnosis of rolling element bearing[J]. Journal of Henan Polytechnic University(Natural Science),2014,33(5): 611-615.(in Chinese)) [6] Wazwaz A, Rach R. Two reliable methods for solving the Volterra integral equation with a weakly singular kernel[J]. Journal of Computational and Applied Mathematics,2016,302: 71-80. [7] Shiki S B, Lopes V, Silva S D. Damage detection in nonlinear structures using discrete-time Volterra series[J]. Key Engineering Materials,2013,569/570: 876-883. [8] 魏瑞轩, 韩崇昭, 张优云, 等. 非线性系统故障诊断的Volterra模型方法[J]. 系统工程与电子技术, 2004,26(11): 1736-1738, 1752.(WEI Rui-xuan, HAN Chong-zhao, ZHANG You-yun, et al. Volterra model method for fault diagnosis of a nonlinear system[J]. Systems Engineering and Electronics,2004,26(11): 1736-1738, 1752.(in Chinese)) [9] Israelsen B W, Smith D A. Generalized Laguerre reduction of the Volterra kernel for practical identification of nonlinear dynamic systems[C]// AIChE Spring Metting 2014.arXiv: 1410. 0741. [10] Mirzaee F, Hamzeh A. A computational method for solving nonlinear stochastic Volterra integral equations[J]. Journal of Computational and Applied Mathematics,2016,306: 166-178. [11] Shiki S B, Jr V L, Silva S D. Identification of nonlinear structures using discrete-time Volterra series[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering,2013,36(3): 523-532. [12] 韩海涛, 马红光, 于宁宇, 等. 基于多音激励的Volterra频域核非参数辨识方法[J]. 西南交通大学学报, 2013,48(2): 250-256.(HAN Hai-tao, MA Hong-guang, YU Ning-yu, et al. Method of identifying Volterra frequency-domain kernels based on stimulus of multi-tone signal[J]. Journal of Southwest Jiaotong University,2013,48(2): 250-256.(in Chinese)) [13] WANG Qi-sheng, WANG Ke-yan, CHEN Shao-jun. Least squares approximation method for the solution of Volterra-Fredholm integral equations[J]. Journal of Computational and Applied Mathematics,2014,272: 141-147. [14] Li S. Classical theory of Runge-Kutta methods for Volterra functional differential equations[J]. Applied Mathematics and Computation,2014,230: 78-95. [15] 朱大奇, 陈尔奎. 旋转机械故障诊断的量子神经网络算法[J]. 中国电机工程学报, 2006,20(5): 132-136.(ZHU Da-qi, CHEN Er-kui. A quantum neural networks fault diagnosis algorithm for rotating machinery[J]. Proceedings of the CSEE,2006,20(5): 132-136.(in Chinese)) [16] 唐高松. 基于Volterra级数模型辨识的旋转机械故障诊断方法研究[D]. 硕士学位论文. 郑州: 郑州大学, 2010: 16-19.(TANG Gao-song. Rotating machine fault diagnosis method based on Volterra series identification[D]. Master Thesis. Zhengzhou: Zhengzhou University, 2010: 16-19.(in Chinese))
点击查看大图
计量
- 文章访问数: 859
- HTML全文浏览量: 90
- PDF下载量: 775
- 被引次数: 0