Enabling Cloud-Based Quality-Data Management Systems

访问、共享和可视化制造企业级数据的基于云的平台正在变得可用。在基于云的质量数据管理系统中，不同设备、产品和设施的质量特性被累积在一个集中的数据库中。这些数据涉及多台机器和多个设施，为实现更有效的质量控制和提高生产率提供了机会。然而，大多数基于云的平台到目前为止还无法利用这些数据中包含的信息来为生产系统控制和质量改进做出更好的决策。该项目的目标是提出一系列方法，以实现对大量质量特征的建模、及时的变化检测、准确的根本原因诊断和最佳维修决策。该项目还将通过为学生提供参与涉及制造、计算、传感和机器学习的跨学科研究的机会，为劳动力培训做出贡献。基于云的平台可能还不能利用制造企业级数据的原因在于缺乏技术来(1)描述质量特征及其关系，以及(2)通过这种描述性模型做出决策。为了实现未来基于云的质量数据管理系统，调查人员将首先提出一种灵活、但严格的分层图形模型所需的方法，该模型将描述不同质量特征之间的相互关系。该模型的分层结构将实现企业内不同设施之间的信息共享。在此描述模型的基础上，研究人员接下来将开发基于似然风险调整和贝叶斯因子理论的过程监控和诊断方法，以及通过部分可观测马尔可夫决策过程(POMDP)框架进行最优维修决策的方法。开发的方法将在从工业合作者那里获得的数据上进行测试。

Cloud-based platforms for accessing, sharing, and visualizing manufacturing-enterprise-level data are becoming available. In a cloud-based quality-data-management system, the quality-characteristics of different devices, products, and facilities are accumulated in a centralized database. These data pertain to multiple machines and multiple facilities, offering opportunities to achieve more effective quality control and productivity improvements. However, most cloud-based platforms are as yet unable to exploit the information contained in such data to make better decisions for production-system control and quality improvement. The objective of this project is to advance a series of methodologies that enable modeling of a large number of quality characteristics, timely change detection, accurate root cause diagnosis, and optimal repair decision-making. The project will also contribute to workforce training by offering students opportunities to engage in interdisciplinary research dealing with manufacturing, computing, sensing, and machine learning. The reason why cloud-based platforms may not as yet exploit manufacturing-enterprise-level data lies in the dearth of techniques to (1) describe the quality characteristics and their relationships, and (2) make decisions informed by such descriptive models. To enable cloud-based quality-data-management systems of the future, the investigators will first advance methodology needed for a flexible, yet rigorous, hierarchical graphical model, which will describe the inter-relationships among different quality characteristics. The hierarchical structure of the model will enable information sharing across different facilities within an enterprise. Based on this descriptive model, the investigators will next develop methodologies for process monitoring and diagnosis via likelihood based risk-adjustment and Bayesian-factor theory, and for optimal repair decisions via Partially Observable Markov Decision Processes (POMDP) framework. The developed methodologies will be tested on data obtained from an industrial collaborator.