Enabling Cloud-Based Quality-Data Management Systems

用于访问、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征被累积在集中式数据库中。这些数据与多台机器和多个设施有关，为实现更有效的质量控制和生产力提高提供了机会。然而，大多数基于云的平台还无法利用这些数据中包含的信息来做出更好的决策，以控制生产系统和提高质量。该项目的目标是推进一系列方法，使大量的质量特性建模，及时的变化检测，准确的根本原因诊断，最佳的维修决策。该项目还将通过为学生提供参与制造、计算、传感和机器学习等跨学科研究的机会，为劳动力培训做出贡献。基于云的平台可能尚未利用制造企业级数据的原因在于缺乏技术来（1）描述质量特性及其关系，以及（2）根据这些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，研究人员将首先推进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。该模型的层次结构将使企业内不同设施之间的信息共享成为可能。基于这个描述性模型，研究人员接下来将通过基于风险调整和贝叶斯因子理论的可能性开发过程监测和诊断方法，并通过部分可观察马尔可夫决策过程（POMDP）框架开发最佳维修决策。将根据从一个工业合作者那里获得的数据对所制定的方法进行测试。

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.