In this paper, the correlation between probability box (p-box) models was studied, and a method of constructing a correlation probability box (cp-box) model from raw bearing data was proposed. First, a copula function was used to construct the correlation model based on bearing data, and the inverse function of the cumulative distribution function was obtained by selecting the appropriate confidence intervals. Then, confidence bounds were obtained to establish the cp-box model. Next, the cp-box model was discretized into a series of focal elements by an average discretization method, and the measurement information of the cp-box model was obtained by an aggregated uncertainty measurement method. Finally, using a support vector machine as a pattern recognition tool, the effectiveness of the cp-box model was evaluated by a comparison with existing methods. The result shows that the copula function can be added to optimize the p-box model.

本文研究了概率盒（p-box）模型之间的相关性，提出了一种从轴承原始数据构建相关概率盒（cp-box）模型的方法。首先，利用copula函数构建基于方位数据的相关模型，通过选择合适的置信区间得到累积分布函数的反函数。然后，获得置信界限以建立cp-box模型。接下来，通过平均离散化方法将cp-box模型离散为一系列焦元，并通过聚合不确定度测量方法获得cp-box模型的测量信息。最后，使用支持向量机作为模式识别工具，通过与现有方法的比较来评估 cp-box 模型的有效性。结果表明可以加入copula函数来优化p-box模型。