摘要
It is difficult to collect the prior information for small-sample machinery products when their reliability is assessed by using Bayes method. In this study, an improved Bayes method with gradient reliability(GR) results as prior information was proposed to solve the problem. A certain type of heavy NC boring and milling machine was considered as the research subject, and its reliability model was established on the basis of its functional and structural characteristics and working principle. According to the stress-intensity interference theory and the reliability model theory, the GR results of the host machine and its key components were obtained. Then the GR results were deemed as prior information to estimate the probabilistic reliability(PR) of the spindle box, the column and the host machine in the present method. The comparative studies demonstrated that the improved Bayes method was applicable in the reliability assessment of heavy NC machine tools.
It is difficult to collect the prior information for small-sample machinery products when their reliability is assessed by using Bayes method. In this study, an improved Bayes method with gradient reliability（GR） results as prior information was proposed to solve the problem. A certain type of heavy NC boring and milling machine was considered as the research subject, and its reliability model was established on the basis of its functional and structural characteristics and working principle. According to the stress-intensity interference theory and the reliability model theory, the GR results of the host machine and its key components were obtained. Then the GR results were deemed as prior information to estimate the probabilistic reliability（PR） of the spindle box, the column and the host machine in the present method. The comparative studies demonstrated that the improved Bayes method was applicable in the reliability assessment of heavy NC machine tools.
基金
Supported by the National Science and Technology Major Project of China(No.2009ZX04002-061)
the National Science and Technology Support Program(No.2013BAF06B00)
the Natural Science Foundation of Tianjin(No.13JCZDJC34000)
