基于贝叶斯网络的制造过程根本原因分析方法

Satyabrata Pradhan, Rajveer Singh, Komal Kachru, S. Narasimhamurthy
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引用次数: 18

摘要

我们描述了一个早期预警系统(EWS),它能够对生产车间和工艺工程部门、产品oem及其分层供应商的质量改进进行根本原因分析。EWS结合了定制设计制造过程领域本体和失效相关知识的使用,以概率约束形式创新地应用领域知识,以及一种新的两步约束优化方法来构建因果网络。概率推理是从因果网络中进行推理的主要工具。该推理引擎提供了在制造场景中进行根本原因分析的能力,因此是汽车EWS的强大武器。该技术具有广泛的适用性,可以在更广泛的制造业的各种环境中使用。
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A Bayesian Network Based Approach for Root-Cause-Analysis in Manufacturing Process
We describe an Early Warning System (EWS) which enables the root cause analysis for initiating quality improvements in the manufacturing shop floor and process engineering departments, at product OEMs as well as their tiered suppliers. The EWS combines the use of custom designed domain ontology of manufacturing processes and failure related knowledge, innovative application of domain knowledge in the form of probability constraints and a novel two step constrained optimization approach to causal network construction. Probabilistic reasoning is the main vehicle for inference from the causal network. This inference engine provides the capability to do a root cause analysis in manufacturing scenarios, and is thus a powerful weapon for an automotive EWS. This technique is widely applicable and can be used in various contexts in the broader manufacturing industry as well.
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