Hybridizing Data and Model Driven Approaches for Proactive Production Control

混合数据和模型驱动方法进行主动生产控制

• 批准号: 1922739
• 负责人: Feng Ju
• 金额: $ 40.07万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922739&HistoricalAwards=false
• 关键词: Hybridizing; Data; Model; Driven; Approaches

This award will contribute to the national prosperity by investigating a framework for factory-level production control using real-time equipment-level sensing data. Rapid advances in sensor technology, computer-controlled processes, high-performance computing, and internet-of-things (IoT) have the potential to improve the productivity of U.S. manufacturing significantly. However, current production systems remain predominately retrospective and responsive to adverse events because real-time analysis has not been sufficiently integrated in proactive decision support. This award investigates an approach based on hybridizing data-driven statistical methods and with product flow models to address the key challenges in real-time sensing, performance prediction, and proactive control of production systems. The knowledge developed from this research will enhance the understanding of the fundamental principles governing manufacturing systems operations from both theoretical and practical perspectives. The research is integrated with an education plan to enhance education and outreach activities in the minority and underrepresented groups.This award supports fundamental research to advance the state-of-the-art in prognostics, data fusion, and real-time production controls at both process and system levels in production environments. The research will investigate a new data-driven approach that combines Bayesian generative models, tensor data analytics, and data-fusion for prognostics to determine a process-level health condition index based on heterogeneous sensing data at different sampling rates. Process-specific health condition information will be integrated with system-level stochastic models for production performance prediction by synthesizing the input from sensor measurements and the output of process-level prediction. Finally, a data-driven sequential decision-making problem will be formulated to derive adaptive control actions that optimize the performance at both system and process levels in real-time. The developed methodology will be tested and validated using data from a small-scale university lab and collaborating industry partners, as well as open data sets published by the National Institute of Standards and Technology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过研究使用实时设备级传感数据的工厂级生产控制框架，为国家繁荣做出贡献。传感器技术、计算机控制流程、高性能计算和物联网（IoT）的快速发展有可能显著提高美国制造业的生产力。然而，目前的生产系统仍然主要是回顾性的，并对不良事件作出反应，因为实时分析还没有充分集成在积极的决策支持。该奖项研究了一种基于混合数据驱动的统计方法和产品流模型的方法，以解决生产系统的实时传感，性能预测和主动控制方面的关键挑战。从本研究中开发的知识将提高从理论和实践的角度对管理制造系统操作的基本原则的理解。该研究与教育计划相结合，以加强少数民族和代表性不足的群体的教育和推广活动。该奖项支持基础研究，以促进生产环境中过程和系统层面的自动化、数据融合和实时生产控制的最新水平。该研究将研究一种新的数据驱动方法，该方法将贝叶斯生成模型，张量数据分析和数据融合相结合，以确定基于不同采样率的异构传感数据的过程级健康状况指数。通过综合传感器测量的输入和过程级预测的输出，特定于过程的健康状况信息将与系统级随机模型集成，用于生产性能预测。最后，将制定一个数据驱动的顺序决策问题，以获得自适应控制行动，优化性能在系统和过程水平的实时。 开发的方法将使用来自小型大学实验室和合作行业伙伴的数据以及美国国家标准与技术研究所发布的开放数据集进行测试和验证。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Deep multistage multi-task learning for quality prediction of multistage manufacturing systems

• DOI: 10.1080/00224065.2021.1903822
• 发表时间: 2021-04
• 期刊: Journal of Quality Technology
• 影响因子: 2.5
• 作者: Hao Yan;Nurrettin Dorukhan Sergin;William A. Brenneman;Steve J. Lange;Shan Ba

Simulation-based Real-time Production Control with Different Classes of Residence Time Constraints

• DOI: 10.1109/case49997.2022.9926676
• 发表时间: 2022-08
• 期刊: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
• 作者: Feifan Wang;Feng Ju

Adaptive Change Point Monitoring for High-Dimensional Data

• DOI: 10.5705/ss.202020.0438
• 发表时间: 2021-01
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Teng Wu;Runmin Wang;Hao Yan;Xiaofeng Shao

Toward a better monitoring statistic for profile monitoring via variational autoencoders

• DOI: 10.1080/00224065.2021.1903821
• 发表时间: 2019-11
• 期刊: Journal of Quality Technology
• 影响因子: 2.5
• 作者: N. Sergin;Hao Yan

Dynamic material deposition control for large-scale additive manufacturing

• DOI: 10.1080/24725854.2021.1956702
• 发表时间: 2021-09
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Sepehr Fathizadan;Feng Ju;Feifan Wang;Kyle Rowe;Nils Hofmann