基于自回归模型的齿轮轴破损诊断  

Autoregressive Model-based Gear Shaft Fault Diagnosis

作　　者：王细洋[1] 孔志高[1] 董海[1] 龚廷恺[1] 

机构地区：[1]南昌航空大学航空与机械工程学院,南昌330063

出　　处：《机械工程学报》2009年第4期265-272,共8页Journal of Mechanical Engineering

基　　金：航空基础科学基金资助项目(2006ZE56007)。

摘　　要：齿轮轴失效引起的齿轮箱振动行为与轮齿失效引起的齿轮箱振动行为不同。传统的齿轮故障诊断方法大多针对于轮齿破损,难以有效识别齿轮轴破损。用自回归模型拟合正常齿轮振动的时域同步平均信号,利用Akaike判据获得自回归模型的阶数,用Levinson-Durbin递归算法求解Yule-Waker方程获得自回归模型的系数。将建立的自回归模型作为线性滤波器处理齿轮箱振动信号,获得预测误差信号。之后对预测误差信号进行两样本Kolmogorov-Smirnov检验,获得正常齿轮轴振动信号和待处理齿轮轴振动信号预测误差的K-S统计距离和相似概率,并将其作为齿轮轴破损特征指标量。实际试验表明这一特征指标的有效性。An autoregressive model-based technique to detect the occurrence and advancement of gear shaft crack is proposed. The order of autoregressive （AR） model is selected by using Akaike information criterion on the time synchronous averaging signal of the gear shaft in its healthy-state, the coefficients of the AR model is determined by the solution of the Yule-Waker equations with the recursion algorithm. The established AR model is then used as a linear filter to process the future-state signal under the same condition. The Kolmogorov-Smimov test is then performed over the prediction error signals. The statistical distance of the test is used as a fault parameter which is capable of diagnosing the gear shaft crack effectively with a higher level of confidence. The other statistical measures such as kurtosis, variance are also analyzed and computed. The performance of the technique is demonstrated by using a full lifetime gear shaft vibration data history.

关 键 词：齿轮轴破损 故障诊断 自回归模型 齿轮箱振动 KOLMOGOROV-SMIRNOV 检验 

分 类 号：TH165.3[机械工程—机械制造及自动化]