Abstract Mechanical product quality depends on many quality characteristics (QCs). There are many coupling relations between these QCs that are required to analyze these relations. A principle − empirical (P–E) model for quality improvement is proposed in this study. The architecture of the model is first introduced. The method of the P–E model structure learning is provided, and the QC relations are determined by empirical data. These discovered QC relations are validated by principal knowledge. The P–E model structure is built based on the validated QC relations. The maximum likelihood estimation (MLE) is used for parameter learning. Finally, a case study is given to demonstrate the P–E model. The results show that the learned structure based on the P–E model is superior to the K2 algorithm when the data size is small. The difference between the P–E model and the K2 algorithm is not significant when the data size is large.

中文翻译：

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基于贝叶斯网络的机械产品开发质量改进原理-经验模型

摘要 机械产品质量取决于许多质量特性（QC）。这些QC之间有很多耦合关系，需要分析这些关系。本研究提出了一种用于质量改进的原则 - 经验（P-E）模型。首先介绍模型的架构。提供了P-E模型结构学习的方法，QC关系由经验数据确定。这些发现的 QC 关系由主要知识验证。P-E 模型结构是基于经过验证的 QC 关系构建的。最大似然估计（MLE）用于参数学习。最后，给出了一个案例研究来证明 P-E 模型。结果表明，当数据量较小时，基于P-E模型的学习结构优于K2算法。