遗传智能采样技术的贝叶斯理论识别滑动轴承-转子系统不平衡量

Bayesian theory using genetic intelligent sampling technique to identify the unbalance parameters of a sliding bearing-rotor system

轴承转子系统不平衡量识别过程中,在输出响应和模型中存在的不确定性参数一般采用概率法描述,通过贝叶斯理论获得不平衡量的联合后验概率密度分布时涉及大量采样.针对采样效率,提出了基于遗传智能采样技术改进贝叶斯理论.首先,以代价函数作为指示因子通过信赖域模型管理方法不断更新先验空间使其覆盖高密度后验空间,然后通过智能布点技术和样本遗传策略以有限的样本点集中呈现在联合后验概率密度分布的高密度区域,提高信赖域上关键区域的精度,从而加快收敛速度,减小耗时的正问题调用次数.最后将其应用于识别具有不平衡量先验信息和带有随机噪声的测试响应的滑动轴承-转子系统的不平衡量,获得不平衡量的均值、置信区间.案例显示能准确快速地抽样,提高了贝叶斯识别的计算效率.

Probability method is used to describe the uncertainty of the output and model in the unbalanced identification process of the sliding bearing-rotor system, the Bayesian theory is used to obtain the joint posterior probability density distribution of the unbalance parameters,which involves massive sampling. A novel algorithm based on genetic intelligent sampling technique is presented to promote the efficiency. In this algorithm, Trust region model management method is firstly used to update the prior space to cover the high-density posterior space by calling the cost function as an indicator. Then the finite sample points are concentrated in the high-density region of joint posterior probability density distribution by intelligent placement technology and sample genetic strategy in order to improve the accuracy of critical areas on trust which can speed up convergence and reduce the number of calling time-consuming positive problem. Finally, the presented method is applied to identify the mean value and confidence interval of the unbalance parameters of the sliding bearing-rotor system, which has unbalanced prior information, and test response with random noise. In the work, the sampling algorithm based on the genetic intelligent sampling technique can promote the efficiency of Bayesian approach for fast identifying the unbalance parameters.