基于降维可视化与Kriging的齿轮振动可靠性分析

针对齿轮振动可靠性分析时计算量大、计算精度低等问题,提出一种基于降维可视化技术和Kriging模型的可靠性分析方法.通过Monte Carlo法生成抽样点,采用降维可视化技术将多维空间降至二维极特征空间,通过Kriging模型预测失效域与安全域的分界线,在预测分界线时,借助Kriging非线性预测和误差分析的特性,通过一种主动学习选点的方式建立Kriging预测模型,来提高样本点的利用率.通过齿轮振动可靠性的算例表明:相比于传统的降维可视化技术,调用极限状态函数由975次减少为149次,计算时间由12400s减小为1810s,可靠度与100000次Monte Carlo模拟计算结果基本吻合一致,验证了该算法的正确性和有效性. 

Reliability analysis of gear vibration based on dimensionalityreduction visualization and Kriging

To solve the problems of large computation and low precision during gear vibration reliability analysis, a reliability analysis method based on dimensionality reduction visualization and Kriging was proposed. Sample points were generated by Monte Carlo method. These points were transformed into two-dimensional pole feature space, and then Kriging model was used to predict the dividing line of safe and failure regions. When predicting the dividing line, an active learning approach of selecting points was introduced to establish Kriging model so that the utilization rate of sample points was improved dramatically, thanks to the properties of nonlinear prediction and error estimation of Kriging. Through gear vibration reliability analysis, and by comparing with traditional dimensionality reduction visualization technique, it is shown that the number of calls to the performance function changes from 975 numbers to 149 numbers, and calculation time changes from 12400s to 1810s. What''s more, the result of this method is consistent with that of 100000 Monte Carlo simulation, so the efficiency and correctness is validated.