一种采用贝叶斯网络的制造过程异常诊断方法

石文栋; 陈富民; 屈发明; 张瑞; 吕春雷

doi:10.7652/xjtuxb201808002

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一种采用贝叶斯网络的制造过程异常诊断方法

更新时间：2025-07-09

- 一种采用贝叶斯网络的制造过程异常诊断方法
- Abnormality Diagnosis Method for Manufacturing Process Based on Bayesian Network
- 西安交通大学学报 2018年52卷第8期页码：9-14
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201808002
  中图分类号： TH16
- 网络首发：2018-08-10，
  
  纸质出版：2018
- 稿件说明：
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石文栋, 陈富民, 屈发明, 等. 一种采用贝叶斯网络的制造过程异常诊断方法[J]. 西安交通大学学报, 2018,52(8):9-14.

Abnormality Diagnosis Method for Manufacturing Process Based on Bayesian Network[J]. 2018, 52(8): 9-14.
石文栋, 陈富民, 屈发明, 等. 一种采用贝叶斯网络的制造过程异常诊断方法[J]. 西安交通大学学报, 2018,52(8):9-14. DOI： 10.7652/xjtuxb201808002.

Abnormality Diagnosis Method for Manufacturing Process Based on Bayesian Network[J]. 2018, 52(8): 9-14. DOI： 10.7652/xjtuxb201808002.

摘要

针对制造过程异常原因复杂、基于传统控制图诊断分析困难等问题

提出了一种采用贝叶斯网络的制造过程异常诊断方法。利用过程异常诊断的先验知识

结合需要诊断的异常特征

构建基于贝叶斯网络的制造过程异常诊断模型。首先提取控制图异常征兆特征

建立贝叶斯网络的异常征兆节点和异常原因节点并进行关联; 然后利用先验知识确定贝叶斯网络中的先验概率

建立贝叶斯网络诊断模型; 最后根据控制图异常特征

利用诊断模型推理异常发生的原因。以汽轮机转子叶轮制造为例进行了诊断

验证了采用贝叶斯网络作为制造过程诊断方法的可行性。

Abstract

Based on Bayesian network

a new method was proposed to diagnose manufacturing process abnormalities

which may be caused by multiple complex reasons. Using the prior knowledge of historical process abnormality diagnosis results

in combination with the abnormal characteristics of present process

the Bayesian network was constructed and the characteristics of the abnormal symptom of the control chart were extracted

the abnormal symptom nodes and the abnormal reason nodes of the Bayesian network were established and correlated. Then the prior distribution in Bayesian network was determined by prior knowledge

and the Bayesian network diagnosis model was determined. According to the abnormal characteristics of the control chart

the reason why the abnormality occurs was diagnosed with the proposed diagnostic model. A turbine impeller manufacturing process was diagnosed

and result validates the feasibility of the proposed method.

关键词

Keywords

references

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