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首页> 外文期刊>Journal of Intelligent Manufacturing >Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems
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Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems

机译：概率布尔网络建模与模型检查作为制造系统DFMEA的方法

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摘要

Modeling manufacturing processes assists the design of new systems, allowing predictions of future behaviors, identifying improvement areas and evaluating changes to existing systems. Probabilistic Boolean networks (PBN) have been used to study biological systems, since they combine uncertainty and rule-based representation. A novel approach is proposed to model the design of an automated manufacturing assembly processes using PBNs to generate quantitative data for occurrence assessment in design failure mode and effects analysis. FMEA is a widely used tool in risk assessment (RA) to ensure design outputs consistently deliver the intended level of performance. Effectiveness of RA depends upon the robustness of the data used. Temporal logic is applied to analyze state successions in a transition system, while interactions and dynamics are captured over a set of Boolean variables using PBNs. Designs are therefore enhanced through assessment of risks, using proposed tools in the early phases of design of manufacturing systems. A two-sample T test demonstrates the proposed model provides values closer to expected values; consequently modeling observable phenomena (). Simulations are used to generate data required to conduct inferential statistical tests to determine the level of correspondence between model prediction and real machine data.
机译：建模制造工艺有助于设计新系统，允许预测未来的行为，识别改进区域并评估现有系统的变化。概率布尔网络（PBN）已被用于研究生物系统，因为它们结合了不确定性和基于规则的代表性。提出了一种新的方法来模拟使用PBNS的自动制造组合过程的设计，以在设计故障模式和效果分析中产生用于发生的定量数据。 FMEA是风险评估（RA）中广泛使用的工具，以确保设计输出一致地提供预期的性能水平。 RA的有效性取决于所使用的数据的稳健性。应用时间逻辑以分析过渡系统中的状态演出，而使用PBNS在一组布尔变量上捕获相互作用和动态。因此，通过对制造系统的早期阶段的阶段进行风险评估来增强设计。两个样本T测试演示了所提出的模型，提供更接近预期值的值;因此，建模可观察现象（）。模拟用于生成进行推断统计测试所需的数据，以确定模型预测和真机数据之间的对应程度。

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