基于小波域广义高斯分布的轴承故障诊断方法  

Bearing Fault Diagnosis Based on Wavelet-domain Generalized Gaussian Distribution

作　　者：陶新民[1] 徐晶[2] 杜宝祥[1] 徐勇[1] 

机构地区：[1]哈尔滨工程大学信息与通信工程学院,哈尔滨150001 [2]黑龙江科技学院数力系,哈尔滨150027

出　　处：《机械工程学报》2009年第10期61-67,共7页Journal of Mechanical Engineering

基　　金：哈尔滨工程大学校科研启动资金资助项目

摘　　要：针对基于小波能量谱和能量谱熵的故障诊断方法要求小波分解系数基本符合高斯分布这一不足,提出一种基于小波系数广义高斯分布参数特征的故障诊断方法。提出的方法分析轴承振动信号多尺度小波分解系数的统计特征,利用广义高斯分布模型对信号的小波分解系数直方图进行拟合,采用最大似然估计方法确定模型参数并以此作为信号特征实现故障诊断。将建议的方法与基于小波能量谱、能量谱熵及小波包的方法进行比较,结果验证设计思想的正确性和算法的高效可检测性。从小波基、窗口宽度和分类器三个层面对建议方法诊断性能的影响进行分析,结果表明提出的方法具有很强的稳定性和鲁棒性。In view of the disadvantage of the fault diagnosis method based on wavelet energy spectrum and energy spectrum entropy which requires wavelet decomposition coefficients to basically conform to Gaussian distribution, a new fault diagnosis method is proposed. This method analyzes the statistics features of the multi-scale wavelet coefficients of beating vibration signals, uses a generalized Gaussian distribution （GGD） model to fit the histogram of wavelet decomposition coefficients of signals, adopts the maximum likelihood method to determine model parameters, and take these as signal characteristics to realize fault diagnosis. The proposed method is compared to methods based on wavelet energy spectrum, energy spectrum entropy and wavelet packet, and the results validate the correctness of design idea and the high-efficiency detectability of algorithm. The influence of wavelet base, window width and classifier on the diagnosis performance of the proposed method is analyzed, and the results show that the proposed method has very high stability and robustness.

关 键 词：故障诊断 小波能量谱 广义高斯分布 最大似然估计方法 

分 类 号：TG156[金属学及工艺—热处理]