CC* Integration-Large: MAINTLET: Advanced Sensory Network Cyber-Infrastructure for Smart Maintenance in Campus Scientific Laboratories

CC* 大型集成：MAINTLET：用于校园科学实验室智能维护的先进传感网络网络基础设施

• 批准号: 2126246
• 负责人: Klara Nahrstedt
• 金额: $ 100万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126246&HistoricalAwards=false
• 关键词: CC; Integration; Large; MAINTLET; Advanced

Studies show that in some industry and scientific environments, between 15 and 60 percent of the total costs originate in maintenance activities, and about 33 cents of every dollar spent on maintenance in the US is wasted because of unnecessary and preventable maintenance activities. The cost of scientific instruments’ maintenance is even greater in universities because the scientific instruments, and associated support equipment, such as vacuum pumps, serve diverse students, staff, and faculty populations for educational and research purposes over much longer periods with smaller budgets than in industry. Instruments’ down-time greatly limits research productivity and programs. Hence, MAINTLET investigates an advanced sensory network cyber-infrastructure with modern AI-guided big data methods that helps the campus scientific laboratories to see patterns that indicate the right time to purchase kits, parts, and services, and minimize opportunity cost due to down-time and all repairs and maintenance.MAINTLET enables cost-effective, scalable, and sustainable reactive, preventive and predictive maintenance solutions for scientific instruments. MAINTLET provides two important indicators. For preventive and predictive maintenance, simulations identify potential instrument failures, using data from instruments’ surrounding sensors such as acoustic sensors, water flow sensors, and contact water temperature sensors. These data help predict in real-time, using AI techniques, when a pump may need condition-based preventive maintenance. For reactive maintenance, trained failure detectors detect failures in real-time. MAINTLET includes sensors; edge devices such as Raspberry Pis executing reactive maintenance services; WiFi and Zigbee access points and networks interconnecting sensors, edge and cloud devices; and a private cloud with predictive and preventive maintenance services. The impact of MAINTLET is in terms of decreased instrument failures and down-time and hence speed-up and accuracy of scientific discoveries, and in terms of security (as uncertainty about failed scientific lab equipment can cause both cyber and physical harm). MAINTLET’s various insights are taught in undergraduate and graduate courses to students from Materials Science & Engineering, Computer Science, and other departments. MAINTLET is presented at the Advanced Materials Characterization Workshop with instrument vendors’ exhibit, “Nano at Illinois” event, and other scientific venues. During the summers, the Worldwide Youth in Science and Engineering program for high school students, and other outreach programs, organized within the Grainger College of Engineering, receive a series of MAINTLET lectures.MAINTLET’s website https://t2c2.csl.illinois.edu/projects/maintlet includes links to data, code, results, and simulations as they are developed. The project-related information will be accessible for at least five years after the project ends.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.

研究表明，在某些工业和科学环境中，总成本的15%至60%来自维护活动，在美国，用于维护的每一美元中约有33美分被浪费在不必要和可预防的维护活动上。科学仪器的维护成本在大学中甚至更高，因为科学仪器和相关的支持设备，如真空泵，为不同的学生，工作人员和教职员工群体提供教育和研究目的，时间要长得多，预算比工业更少。仪器的停机时间极大地限制了研究生产力和项目。因此，MAINTLET研究了一种先进的传感网络基础设施，采用现代人工智能引导的大数据方法，帮助校园科学实验室看到指示购买套件，零件和服务的正确时间的模式，并最大限度地减少停机时间和所有维修和维护的机会成本。MAINTLET实现了具有成本效益，可扩展和可持续的反应，科学仪器的预防性和预测性维护解决方案。MAINTLET提供了两个重要指标。 对于预防性和预测性维护，模拟使用来自仪器周围传感器（如声学传感器、水流传感器和接触式水温传感器）的数据来识别潜在的仪器故障。这些数据有助于使用人工智能技术实时预测泵何时需要基于状态的预防性维护。 对于反应性维护，经过培训的故障检测器可实时检测故障。MAINTLET包括传感器;边缘设备，如执行反应性维护服务的Raspberry Pis; WiFi和Zigbee接入点以及连接传感器、边缘和云设备的网络;以及具有预测性和预防性维护服务的私有云。 MAINTLET的影响在于减少仪器故障和停机时间，从而加快科学发现的速度和准确性，以及安全性（因为科学实验室设备故障的不确定性可能导致网络和物理伤害）。 MAINTLET的各种见解在本科和研究生课程中教授给来自材料科学工程，计算机科学和其他部门的学生。MAINTLET在先进材料表征研讨会上与仪器供应商的展览，“纳米在伊利诺伊州”活动，和其他科学场所。在夏季，世界青年科学与工程计划的高中生，和其他外展计划，在工程的格兰杰学院内组织，收到一系列的MAINTLET讲座。MAINTLET的网站https://t2c2.csl.illinois.edu/projects/maintlet包括链接到数据，代码，结果，和模拟，因为他们的发展。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

Echoes of the Past: A Journey to Re-Create the Invention of the Transistor and the Earliest Transistor Circuit: Appreciating the Technical Challenges and Genius of Bardeen and Brattain

过去的回声：重新创造晶体管和最早的晶体管电路的发明之旅：欣赏巴丁和布拉顿的技术挑战和天才

• DOI: 10.1109/med.2023.3257144
• 发表时间: 2023
• 期刊: IEEE Electron Devices Magazine
• 作者: Kaufman, Robert B.;Kesler, Benjamin;O’Brien, Thomas;Su, Patrick;Dallesasse, John M.
• 通讯作者: Dallesasse, John M.

Low-threshold InP quantum dot and InGaP quantum well visible lasers on silicon (001)

• DOI: 10.1364/optica.443979
• 发表时间: 2021-11-20
• 期刊: OPTICA
• 影响因子: 10.4
• 作者: Dhingra, Pankul;Su, Patrick;Lee, Minjoo Larry
• 通讯作者: Lee, Minjoo Larry

High-power single-mode vertical-cavity surface-emitting lasers using strain-controlled disorder-defined apertures

使用应变控制无序定义孔径的高功率单模垂直腔表面发射激光器

• DOI: 10.1063/5.0068713
• 发表时间: 2021
• 期刊: Applied Physics Letters
• 影响因子: 4
• 作者: Su, Patrick;Pikul, Kevin P.;Kraman, Mark D.;Dallesasse, John M.
• 通讯作者: Dallesasse, John M.

SENSELET++: A Low-cost Internet of Things Sensing Platform for Academic Cleanrooms

SENSELET：用于学术洁净室的低成本物联网传感平台

• DOI: 10.1109/mass52906.2021.00020
• 发表时间: 2021
• 期刊: 2021,
• 作者: Tian, Beitong;Yang, Zhe;Moeini, Hessam;Gupta, Ragini;Su, Patrick;Kaufman, Robert;McCollum, Mark;Dallesasse, John;Nahrstedt, Klara
• 通讯作者: Nahrstedt, Klara