CAREER: Collaborative Modeling for Distributed Sensing and Real-time Intelligent Control to Improve Battery Manufacturing Productivity and Efficiency

该学院早期职业发展（Career）计划奖是一项以协同传感信息处理和电池制造智能在线控制框架为中心的综合研究和教育研究。它旨在将决策理论与实时控制相结合，这将允许分布式传感器网络的全部潜力，以提高生产系统的响应能力、生产力和整体效率。该方法包括以下任务：1)建立虚拟多层感知框架和基于事件的建模，直接建模分布式传感器系统；2)开发协同信息处理框架，有效识别系统暂态损耗和系统低效的根本原因，创建系统学习方法，优化传感器网络设计；3)建立集成决策理论和控制的智能在线控制方法，实现自适应资源分配和分布式管理；4)通过与行业合作伙伴的协作，通过仿真和真实数据验证这些方法。如果成功，该CAREER项目将通过为分布式传感器网络的系统级实时决策能力提供新的概念、标准和算法，推动分布式传感器网络的发展，并为下一代工厂范围的传感、信息处理和控制创造一种可行的方法。实现这些目标将大大降低成本，减少停机时间，提高质量和系统效率，提高美国工业和国家经济的竞争力。职业教育计划将通过课程和实验室开发、解决现实问题和其他拓展活动，为劳动力培训做出积极贡献，重点是扩大代表性不足群体的参与。通过会议/期刊出版物和国际/工业合作的传播将导致这些研究成果暴露于广泛的分布式传感器网络，这对国家的经济增长至关重要。

This Faculty Early Career Development (CAREER) Program award is an integrated research and education study centered on collaborative sensing information processing, and an intelligent online control framework for battery manufacturing. It aims to integrate decision theory with real-time control, which will allow the full potential of distributed sensor networks to improve production system responsiveness, productivity and overall efficiency. The methodology includes the following tasks: 1) establish a virtual multi-layer sensing framework and event-based modeling to directly model distributed-sensor systems, 2) develop a collaborative information processing framework to effectively identify system transient loss and root causes of system inefficiency, and create a system learning method to optimize sensor network design, 3) establish an intelligent online control method to integrate decision theory with control for adaptive resource allocation and distributed management, and 4) validate these methods using simulation and real data through collaboration with industrial partners. If successful, this CAREER project will advance the state of the art on distributed sensor networks by contributing new concepts, criteria, and algorithms to its system level real-time decision making capabilities, and create an enabling methodology for next-generation plant-wide sensing, information processing and control. Accomplishing these goals will lead to dramatic cost reductions as a result of reduced downtime, improved quality and system efficiency, boosting the competitiveness of U.S. industries and the nation's economy. The CAREER education program will make a positive contribution to workforce training through curriculum and lab development, real-world problem solving and other outreach activities, with a focus on broadening participation of underrepresented groups. Dissemination through conference/journal publications and international/industrial collaborations will lead to exposure of these research results to a wide range distributed sensor networks that are of vital importance to the nation's economic growth.