基于改进贝叶斯的重型数控机床可靠性研究

重型数控机床在机械加工领域占据重要地位，因此提高其可靠性以及加工精度，对我国工业发展有重要意义。重型数控机床具有结构复杂、故障溯源困难、样本少、数据不足等缺点，因此对其进行可靠性研究比较困难。针对这一问题，采用双参数的威布尔分布建立机床的可靠性模型，引入贝叶斯理论对其进行参数估计，并通过马尔科夫链蒙特卡洛方法(MCMC)计算参数估计结果。对贝叶斯参数估计法中的待估参数进一步分析，得到多层次的贝叶斯模型，并通过参数仿真实验分析其准确性。采用标准均方根误差值及置信区间宽度进行模型优劣的对比，结果表明，改进后的贝叶斯方法参数估计结果精度更优，更有利于建立机床可靠性模型。

Reliability Research of Heavy CNC Machine Tools Based on Improved Bayesian

Heavy-duty CNC machine tools occupy an important position in the field of machining, and improving its reliability and machining accuracy is of great significance to the industrial development of China. Compared with conventional machine tools, heavy-duty CNC machine tools have the characteristics of complex structure, difficulty in fault tracing, few samples, and insufficient data, which make it difficult to conduct reliability research on them. Aiming at this problem, this paper uses the Weibull distribution of two parameters to establish the reliability model of the machine tool, introduces Bayesian theory to estimate its parameters, and calculates the parameter estimation results through the Markov Chain Monte Carlo method (MCMC). In order to improve the accuracy of parameter estimation, the traditional Bayesian method is improved, and the standard root mean square error value and confidence interval are used for evaluation and comparison. The results show that the improved Bayesian method has better parameter estimation accuracy and is more conducive to the establishment of machine tool reliability models.