Bayesian network model with dynamic structure identification for real time diagnosis

D. Nguyen, Q. Duong, E. Zamaï, M. Shahzad
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引用次数: 5

Abstract

This paper proposes a method for real time diagnosis against product quality drifts in an automated manufacturing system. We use Logical Diagnosis model to reduce the search space of suspected equipment in the production flow, which is then formulated as a Bayesian network to compute risk priority for each equipment, using joint and conditional probabilities. The objective is to quickly and accurately localize the possible fault origins and support effective decisions on corrective maintenance. The key advantages offered by this method are (i) reduced unscheduled equipment breakdowns, and (ii) increased and stable production capacities, required for success in highly competitive and automated manufacturing systems. Moreover, this is a generic method and can be deployed on fully or semi automated manufacturing systems.
具有动态结构识别的贝叶斯网络模型用于实时诊断
提出了一种针对自动化制造系统中产品质量漂移的实时诊断方法。我们使用逻辑诊断模型来减少生产流程中可疑设备的搜索空间,然后将其表示为贝叶斯网络,使用联合概率和条件概率计算每个设备的风险优先级。目标是快速准确地定位可能的故障来源,并支持对纠正性维护的有效决策。该方法提供的主要优势是:(1)减少计划外设备故障,(2)提高和稳定的生产能力,这是在高度竞争和自动化制造系统中取得成功所必需的。此外,这是一种通用方法,可以部署在全自动化或半自动化制造系统上。
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