中文摘要：

为了诊断出强噪声干扰下的齿轮故障,提出时域同步平均技术与AR模型相结合的齿轮故障诊断方法。用TSA技术提取强噪声干扰下的齿轮特征信号,用FPE准则确定AR模型的阶次,利用AR模型参数算法确定齿轮正常状态下参数向量及参数容差范围,然后在模型阶次不变的情况下分析齿轮故障信号的AR模型参数,对比建立的参数容差范围,从而诊断齿轮故障。将该方法对实际试验信号进行分析,对提取到的8组正常齿轮特征信号数据建立AR模型,优化AR模型的最佳阶次为5阶,由AR模型参数算法得到了正常齿轮的AR模型参数向量及参数容差范围,再用同样阶数为5阶的AR模型分析了故障状态下的几组模型参数,对比建立的正常AR模型参数容差范围,从而诊断出齿轮故障。

英文摘要：

In order to diagnose the fault gear under the strong noise interference, put forward the technique of synchronous time domain average （TSA） combined with AR model of gear fault diagnosis methods. The TSA technology was used to extract gear characteristic signal under strong noise interference, the order of AR model is determined by FPE criteria, the parameter vector and tolerance range of parameter of gear under the normal state is determined by AR model parameter algorithm, and then analysis of AR model parameter of the gear fault signal under the same model order, contrast to established tolerance range of parameter, so as to gear fault diagnosis. Applying this method to actual test signal analysis,the extraction of the characteristics of eight groups of normal gear signal data AR model is established,the best order for five order AR model, by the AR model parameters algorithm to get the AR model parameter vectors and parameters of the normal gear tolerance range, then in the same order number for five order of AR model analyzes the fault condition of several groups of model parameters, contrast to established tolerance range of normal AR model parameters, the result shows that it is effective to gear fault diagnosis.