基于灰色关联度和Teager能量算子的轴承早期故障诊断

滚动轴承的健康状态直接影响着旋转设备的运行状态,为了及早获取滚动轴承状态异常的信息,提出了基于灰色关联度和Teager能量算子(TEO)的滚动轴承早期故障的诊断方法。对滚动轴承运转的振动数据进行等长度分组,计算各组数据与轴承状态良好的第一组数据之间的灰色关联度,根据灰色关联度值的变化趋势,确定早期故障发生的时间段,截取该时段的振动数据进行Teager能量包络谱分析,确定故障类型。分别采用峭度系数、波形因子和均方根等指标与TEO相结合的方法对相同的轴承振动信号进行了分析和故障诊断,将各种方法的早期故障诊断结果与所提方法比较,结果验证了"灰色关联度+TEO"方法在轴承早期故障诊断中的可行性及有效性。

Early fault diagnosis of rolling bearing based on GRD and TEO

Health status of rolling bearing directly affects operational state of rotary equipment. In order to acquire abnormal status information of rolling bearing as soon as possible, a method for early fault diagnosis of rolling bearing was proposed based on the grey relational degree (GRD) and Teager energy operator (TEO). Dividing vibration data of a rolling bearing into equal length sets, computing GRD values between the first data set with good bearing condition and other ones, according to GRD changing trend, the time interval for early faults occurring was determined. The vibration data in this time interval were intercepted and TEO envelope spectral analysis was conducted for them to judge faults. The same bearing vibration data were also analyzed and diagnosed with TEO method combined with indexes, such as, kurtosis coefficient, waveform factor and root mean square, respectively. The early fault diagnosis results with these methods were compared with those using the proposed GRD+TEO one. Results showed that the feasibility and effectiveness of the proposed GRD+TEO method in early fault diagnosis of rolling bearing are verified.