基于广义贝叶斯法则的机床热误差分析(英文)

Thermal Error Analysis in Machine Tools Based on Generalized Interval Bays’rule

在传统的贝叶斯法则概率计算中,偶然与认知不确定性没有区分,而在广义贝叶斯法则中,偶然不确定性表述为概率测量,认知不确定性通过区间来描述。以机床轴承热误差分析为例说明了广义贝叶斯法则概率应用的有效性。与传统的贝叶斯法则进行了比较。结果表明:用广义贝叶斯法则能更好地描述热误差。

In traditional Bays＇ rule of probability calculation, epistemic uncertainty and aleatory uncertainty are not distinguished. Recently, a generalized interval Bays＇ rule （GIBR） based on the model interval was proposed. In GIBR, aleatory uncertainty is represented as probability measure; epistemic uncertainty is captured by interval. To demonstrate the effectiveness of GIBR, a case study of thermal error analyzing in machine tools of bearing is presented. The results show that the GIBR has a better performance to characterize thermal errors than that of the traditional Bays＇ rule.