滚动轴承振动的灰自助动态评估与诊断

Dynamic assessment and diagnosis of vibration of rolling bearings using grey bootstrap

以灰预报和自助再抽样方法为基础,提出灰自助动态评估与诊断模型,以评估和改进滚动轴承的振动.该模型用动态不确定度、估计区间、估计真值、平均不确定度、平均真值和系统误差测度等6个参数,描述滚动轴承振动的基本特征.对HKRB轴承的试验表明,该模型对随机误差的概率分布与系统误差的类型没有任何要求,在平均不确定度为最小的条件下分离出系统误差.评估的可信度达到100%.根据灰自助动态评估结果,还诊断出影响轴承振动的误差特征,揭示了来自轴承零件制造的误差根源,为实施滚动轴承振动的生产过程控制奠定了基础.

Based on grey prediction and bootstrap resampling, a model of dynamic assessment and diagnosis is proposed to evaluate and improve vibration of rolling bearings. The model characterizes vibration of rolling bearings by developing six parameters, viz. dynamic uncertainty, estimated interval, estimated true value, mean uncertainty, mean true value, and systematic error measure. Experiments on HKRB bearing show that the model can separate systematic errors with a minimum of the mean uncertainty, without any requirements for both the probability distribution of random errors and the type of systematic errors. Reliability of assessment is proved to be 100%. The model also discovers the effect of error character on vibration of rolling bearings and then lays a foundation of control over vibration of rolling bearings in the process of production, by revealing error root from making the parts of rolling bearings.