SRSL

System Reliability and Safety Laboratory @ Iowa State University (ISU)

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    • Engineering Design under Uncertainty
    • Prognostics and Health Management
    • Design for Failure Prevention of Lithium-Ion Battery
    • Design for Resilience
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    • Neware BTS4000 Battery Test System
    • Novonix High Precision Charger
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Welcome to the System Reliability and Safety Laboratory!

About Us

The System Reliability and Safety Laboratory (SRSL) is one of the research laboratories in the Department of Mechanical Engineering at Iowa State University. The focus of our research work is developing new engineering design and failure prognostics methodologies for the design of safe, long-lasting and low-cost engineered systems. We have been able to develop new simulation-based design methods and sensor-based failure prognostics techniques that can be used to design systems with reduced life-cycle costs and improved reliability and safety. Click here for a detailed description of our focused research areas.

Engineering Design under Uncertainty Prognostics and Health Management Prognostics of Li-Ion Battery Design for Resilience
Engineering Design under Uncertainty
Prognostics and Health Management (PHM)
Prognostics of Lithium-Ion Battery
Design for Resilience

Group Members

From left to right: Cole Tenold, Stetsen Greiner, Yifei Li, Ha Lim Jeong, Mohammadkazem Sadoughi, Jajun Ryu, Austin Downey, and Dr. Chao Hu

Recent Posts

  • Recent NSF Award for Intuitive Predictive Maintenance of Industrial and Agricultural Equipment December 30, 2020
  • Rising Stars Spotlight: Venkat Nemani and Jinqiang Liu December 18, 2020
  • Research on Machine Health Monitoring Highlighted December 17, 2020
  • Todd and Rohan Joined Our Group! November 25, 2020

Contact Information

2529 Union Dr
Ames, IA 50011
Lab Location: 2081 Black Engineering
Email: chaohu@iastate.edu
Phone: +1-515-294-0771

About SRSL

The System Reliability and Safety Laboratory (SRSL) is a research laboratory dedicated to improving the reliability and safety of engineered systems. We and our collaborators have developed new methods for simulation-based design and sensor-based prognostics that can be used to design engineered systems for failure resilience.

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