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



Klara Nahrstedt – Coordinated Science Laboratory (CSL )

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

Mark McCollum – Holonyak Micro-and-Nanotechnology Laboratory (HMNTL)

Mauro Sardela –  Materials Research Laboratory (MRL)

Giani Pezzarossi – Engineering IT Shared Services (Engrit)


  • Beitong Tian
  • Ragini Gupta
  • Zhe Yang
  • Patrick Su
  • Robert Bruce Kaufman
  • Mark David Kraman
  • Hessam Moeini
  • Cody Wang
  • Janam Bipinbhai Bagdai

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; 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 are performed. These vacuum 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, and 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 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.

Hardware Platform:

Customized Data Acquisition System Used by MAINTLET

Customized Vibration Sensor

To reduce the cost of the vibration sensor, we customized the vibration for the academic cleanroom environment. We use an accelerometer to acquire the vibration acceleration and use a metal plate, and adhesives to attach the vibration sensor to the pump. The experiment result shows this low-cost vibration sensor can capture vibration with frequency from 100 to 20KHz. At the same time, it has 10X less cost than industrial ones on the market.


Sensing Terminal

The Sensing Terminal consists of a Raspberry Pi Zero with a customized sensing array hat and a SENSELET interface. It is used to record environmental temperature, humidity, surface temperature (via SENSELET interface), vibration, magnetic field, and audio signals (via customized Sensing Array).


Microphone Array

The Microphone Array consists of a Raspberry Pi 4 with a microphone array hat. It is used to record multi-channel audio signals with which we can detect pump failures. 




Data Collection Preliminary Results:

Data collected from MAINTLET data acquisition system


Pump vibration amplitude changes in frequency domain after 15 days


  • 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.

Other Links:

  1. Github:

Acknowledgment: 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.