时域新指标和PNN在滚动轴承故障诊断中的应用

New Time Domain Index and Probabilistic Neural Network and Their Application in Fault Diagnosis of Rolling Bearing

针对传统时域指标在滚动轴承信号特征提取时分类精度不高的问题。首先,选取适合在线简单快速判别的时域指标,并根据轴承疲劳损伤大小和局部损伤数量增加,分析时域指标对故障的敏感性;其次,融合传统时域指标,得到了两个更为敏感的时域新指标TALAF和THIKAT;最后,利用实时性较好的概率神经网络训练和测试包括两个新指标的数据集,并与未加入新指标的数据集训练和测试结果进行比较,仿真结果验证了TALAF和THIKAT指标有效提高了轴承故障诊断的准确性。

Aiming at lower accuracy of classification for signal feature extraction of rolling bearing,firstly,some time domain indexes for online simple rapid discrimination are selected. Sensitivity of time domain index of fault is analyzed based on size of bearing fatigue damage and number of local damage. Secondly,the traditional time domain indexes are fused to calculate new sensitive time domain indexes ‘TALAF’and ‘THIKAT’. Lastly,the data set including two new indicators are trained and tested based on probabilistic neural network（ PNN） which has a good real-time. The training and testing results for the traditional time domain indexes are compared with results of the data set including two new indicators. Simulation results show that TALAF and THIKAT can effectively improve the accuracy of classification index in fault diagnosis of rolling bearing.