Planning Grant: I/UCRC for Intelligent Maintenance Systems at The University of Texas at Austin

规划补助金：I/UCRC for Intelligent Maintenance Systems（IMS）1161021德克萨斯大学奥斯汀分校; Dragan Djurdjanovic德克萨斯大学奥斯汀分校（UT-A）正在寻求加入现有的I/UCRC for Intelligent Maintenance Systems（IMS），目前由辛辛那提大学（牵头）、密歇根大学和密苏里州科技大学组成。通过加入现有的IMS中心，UT-A将以UT教师和学生的专业知识和研究兴趣为基础的新思想和焦点来扩大正在进行的IMS研究。这些新方向包括半导体制造、汽车技术、石油工业、可再生能源、发电厂和生物医学的预测性维护。在拟议的网站的研究将通过以下推力实现：1）信息理论的方法来状态监测; 2）数据驱动的方法，用于运行状态依赖的状态监测和预测3）容错控制的复杂工程系统; 4）预测性维护方法和范例在生物医学和5）系统操作的基础上的信息系统组件的条件。 美国经济花费超过1万亿美元用于维护关键资产，其中超过33%的成本浪费在无效的维护上。UT-A打算通过贡献新的想法和利用许多新行业的投入和经验来解决这个问题，这些行业没有从预测性维护研究中受益。评估和预测工程系统状况的能力的预期进步将增加弹性并改善未来机器的操作，由于更有效的维护干预而减少资源浪费，并且由于设备的更高可用性而提高使用率和生产率。预计将在UT-A进行的研究的更广泛的影响将通过与工业合作伙伴的定期会议，向工业和工业界开放的诊断和维护短期课程，定期参与和出版相关会议以及与美国和国外大学的合作伙伴关系来实现。

Planning Grant: I/UCRC for Intelligent Maintenance Systems (IMS) 1161021 University of Texas-Austin; Dragan Djurdjanovic The University of Texas-Austin (UT-A) is seeking to join the existing I/UCRC for Intelligent Maintenance Systems (IMS) currently comprised of the University of Cincinnati (lead), the University of Michigan and the Missouri University of Science and Technology. By joining the existing IMS Center, the UT-A will expand the ongoing IMS research with new ideas and foci grounded in the expertise and research interests of the UT faculty and students. Those new directions include predictive maintenance in semiconductor manufacturing, vehicle technologies, oil industry, renewable energy, power plants and biomedicine. The research in the proposed site will be realized through the following thrusts: 1) Information-theoretical approaches to condition-monitoring; 2) Data-driven methods for operating regime-dependent condition monitoring and prediction 3) Fault-tolerant control of complex engineering systems; 4) Predictive maintenance methods and paradigms in biomedicine and 5) System operations based on the information about the condition of system components. The US economy spends more than $1 trillion for maintenance of critical assets, with more than 33% of these costs being wasted on ineffective maintenance. The UT-A intends to address this problem by contributing new ideas and leveraging inputs and experiences from numerous new industries that did not benefit enough from the predictive maintenance research. Expected advances in the ability to assess and predict the condition of an engineering system will increase the resilience and improve operation of machines in the future, with reduced waste of resources due to more effective maintenance intervention, and improved usage and productivity due to higher availability of equipment. Broader impacts of the research expected to take place at UT-A will be realized through regular meetings with industrial partners, short courses on diagnostics and maintenance open to industry and academe, regular participation and publication in relevant conferences, and partnerships with universities in the US and abroad.