SRSL

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

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Austin Joined USC as Faculty Member

July 30, 2018August 15, 2018chaohu

Dr. Austin Downey, a former member of our group, has joined the Department of Mechanical Engineering at the University of South Carolina as a tenure-track Assistant Professor, starting in Fall 2018. Congrats!

Austin Recognized for Zaffarano Award and Research Excellence Award

May 4, 2018December 11, 2018meng

Austin Downey, who is a Ph.D. candidate in our group, recently received an honorable mention for the Iowa State’s Zaffarano Award and Research Excellence Award, both of which recognize excellence in graduate research. Congratulations!

Q&A with Austin Downey

Dr. Zhixiong Li Joined Our Group

September 19, 2016August 29, 2019chaohu

Dr. Zhixiong Li joined our group as a Postdoc Research Associate. Welcome, Dr. Li!

Meng and Sheng Joined Our Group!

August 19, 2016August 29, 2019chaohu

Meng Li and Sheng Shen joined our group for their Ph.D. studies in Mechanical Engineering. Welcome, Meng and Sheng!

Yifei Joined Our Group!

January 12, 2016August 29, 2019chaohu

Yifei Li joined our group for his M.S. study in Mechanical Engineering. Welcome, Yifei!

Mohammad Joined Our Group!

September 25, 2015August 29, 2019chaohu

Mohammad Kazem Sadoughi joined our group for his Ph.D. study in Mechanical Engineering. Welcome, Mohammad!

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Recent News

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

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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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