MAINTLET: Advanced Sensory Network Cyber-Infrastructure for Smart Maintenance in Campus Scientific Laboratories

Principal Investigators (PIs):

Klara Nahrstedt – Coordinated Science Laboratory (CSL)

John Dallesasse – Holonyak Micro-and-Nanotechnology Laboratory (HMNTL)

Mauro Sardela –  Materials Research Laboratory (MRL)

Gianni Pezzarossi – Engineering IT Shared Services (Engrit)


  • Beitong Tian
  • Cody Wang
  • Robert Bruce Kaufman
  • Janam Bipinbhai Bagdai
  • Leah Espenhahn
  • Logan Keating
  • Ahmadreza Eslaminia
  • Ragini Gupta
  • Zhe Yang (Alumni)
  • Patrick Su (Alumni)
  • Hessam Moeini (Alumni)
  • Mark David Kraman (Alumni)

Project Description:

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; vibration sensors, audio microphones, 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.

As part of the MAINTLET’s objective, fault diagnosis and health monitoring of scientific cleanroom equipment such as vacuum pumps is performed. These vaccum pumps play an instrumental role in semiconductor manufacturing industries and are highly prone to failures due to overloading, overheating or mechanical wear/tear over time. In order to ensure reliability and reduced maintenance costs, it is important to detect faults early and accurately. Subsequently, a network of sensors including surface temperature, current , vibration , microphone (sound sensor) is implemented for continuous status monitoring of the vacuum pumps. By studying frequency change of vibration signals and ML based acoustic data analysis, the collected sensory data is correlated with the operational performance of the pump. This helps in proactively identifying and alerting any type of faults/failures within the vacuum pumps in real-time, thus avoiding any kind of unplanned downtime or outages within scientific laboratories.  In order to provide a more comprehensive performance monitoring of cleanroom equipment, digital twins of vacuum pumps are designed which are the real-time replicas of the physical assets deployed within cleanrooms. These digital twins can empower the users to simulate different “What-if” failure scenarios for the actual pump which can be analyzed to understand how the pump will perform in different environments, situations and stressors, and subsequently predictions can be made for the corrective actions or adjustments needed to achieve the desired performance in real-time.

MAINTLET’s website 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.

Funding Agencies:

 This research is funded by the National Science Foundation, NSF OAC 21-26246, ” CC* Integration-Large” MAINTLET: Advanced Sensory Network Cyber-Infrastructure for Smart Maintenance in Campus Scientific Laboratories”. Any results and opinions are our own and do not represent views of National Science Foundation.


  • (2021). SENSELET++: A Low-cost Internet of Things Sensing Platform for Academic Cleanrooms. In Proceedings of the IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems [MASS2021].
  • Klara Nahrstedt, Ragini Gupta, Beitong Tian, Zhe Yang, Patrick Su, Robert Kaufmann, Xiaoyang Wang, Cody Wang, Leah Espenbahn, Ahmadreza Eslaminia, John Dallesasse, Gianni Pezzarossi, Mauro Sardela, “Sensing and Computing Challenges in Academic Ultra-Clean Environments for Enhanced Data Integrity”, Open Access article, University of Illinois Urbana-Champaign, 2022.