基于混沌理论滚动轴承振动稳健化试验数据的动态分析

提出改进的Huber M方法是以中位数和Huber M方法两种稳健化处理相融合的一种对数据进行稳健化处理的方法.用中位数和数据平均值相似度判断数据是否有变异,根据变异率变化趋势确定变异率.发现在0%~10%变异率范围内,滚动轴承振动数据的连续性和可信度随着变异率的增加而增强.用混沌理论分析滚动轴承的动态特性,发现同一批次的滚动轴承振动有相同的时间延时、嵌入维数、最大Lyapunov指数,其中最大Lyapunov指数均大于0即属于混沌特征,进一步计算最大可预测周期,最大可预测周期为667个单位.滚动轴承振动时间序列物理空间的中位数和相空间的估计关联维数为非线性非单调性的内在运行机制,为滚动轴承振动的动态分析进一步提供可靠的依据.

Dynamic analysis of the robust test data on rolling bearing vibration based on chaos theory

The improved Huber M method which fuse to two robust processings of median and Huber M method was proposed to solve the robust processing of data. The similarity of the median and average value was given to judge whether the variation data exist, the variation rate were determined by trend of the variation rate. Within the variation rate range of 0%-10%, the continuity and credibility of the rolling bearing vibration data increase with the increasing of the variation rate. The dynamic characteristics of rolling bearings were analyzed by using chaos theory. Time delay, embedding dimension and the maximum Lyapunov index of vibration data are the same on the same batch of rolling bearing. And the maximum Lyapunov index which is bigger than 0, show that the dynamic characteristic on rolling bearing vibration belongs to chaos characteristics, moreover the maximum forecast cycle are calculated, namely 667 unit. The nonlinear and monotonicity of the median of physical space and estimated correlation dimension of phase space on rolling bearing vibration time series is the inner running mechanism on rolling bearing vibration, which provide reliable basis for further analysis.