【摘要】 针对滚珠丝杠故障诊断中存在大量冗余信息的特点,引入平均影响值法对故障信号特征进行筛选。该法可剔除冗余特征,保留对诊断结果影响较大的特征作为支持向量机(SVM)的输入,然后借助支持向量机实现对输入参量的训练以及故障模式识别。经实验验证,实例中所建立的滚珠丝杠故障诊断模型,能在更大程度上缩短诊断时间,提高分类精度,具有较传统诊断模型更好的诊断效果。更多还原

【Abstract】 According to the characteristic that a large number of redundant information existed in the fault diagnosis of ball screw, mean impact value( MIV) method was introduced to select features of trouble signal. Using this method,redundant features could be removed and the features that had greater impact on the diagnostic result were taken as the input of support vector machine( SVM). Then SVM was used to train input parameters and complete fault pattern recognition. By experiments verification,the ball screw fault diagnosis model has a better diagnostic result which has shorter time and high accuracy than before.更多还原