Data analysis and resource allocation in Bayesian selective accelerated reliability growth

Abstract

The rapid pace of technology advancement has resulted in increasingly complex systems with more potential failure modes. However, it is quite common that multiple key components of such a system may be developed, tested and improved independently during product development. Without taking a holistic approach to system reliability improvement, a significant amount of time and resources may be wasted on over-design of some components, which can be otherwise used for strengthening other under-designed components. The technical challenge is more prominent when accelerated testing is utilized in a reliability growth program in hopes of shortening the system development cycle. To overcome limitations of the traditional reliability growth method using the Crow-AMSAA model, a Bayesian selective accelerated reliability growth method is proposed in this article to accelerate potential failure modes and aggregate component testing results and prior knowledge for predicting system reliability growth and corrective actions. As one of the key steps, the method dynamically allocates limited resources for testing and correcting failures on all system levels. Numerical examples illustrate that the proposed integrated statistical and optimization method is effective in estimating and improving the overall reliability of a system.

Keywords:

• Bayesian methods
• selective accelerated testing
• resource allocation
• reliability growth

Additional information

Notes on contributors

Cesar Ruiz

Cesar Ruiz is a Ph.D. candidate in the Industrial Engineering Department at the University of Arkansas. He received his Bachelor’s degree in business engineering from Superior School of Business and Economics, El Salvador. His research interests are in reliability modeling, Bayesian statistics, selective maintenance, and spare-parts inventory control. His research has been published in Quality and Reliability Engineering International and IISE Transactions. He was a finalist in the QCRE best student paper competition during IISE annual meeting 2017 & 2018. He is a student member of IISE, SRE, and INFORMS.

Mohammadhossein Heydari

Mohammadhossein Heydari works as a Data Scientist at the Cox Automotive Inc. He holds a Ph.D. in industrial engineering from the University of Arkansas. He also received a Master’s degree in industrial engineering from the Sharif University of Technology and a Bachelor’s degree in industrial and systems engineering from the Isfahan University of Technology. His research interests are large-scale optimization and statistical analysis with applications in reliability, healthcare, and maintenance.

Kelly M. Sullivan

Kelly Sullivan is an assistant professor in the Industrial Engineering Department at the University of Arkansas. He holds a Ph.D. in industrial and systems engineering from the University of Florida and an M.S. in industrial engineering from the University of Arkansas. His research interests center on advancing computational methodology for designing, maintaining, and securing complex systems. His work focuses on advancing relevant knowledge in the areas of system reliability and network optimization. Dr. Sullivan received a National Science Foundation CAREER Award in 2018 and was awarded the 2014 Glover-Klingman Prize for the best paper published in Networks. He is a member of IISE and INFORMS, and he serves as an associate editor for Operations Research Letters.

Haitao Liao

Haitao Liao is a Professor, and John and Mary Lib White Endowed Systems Integration Chair in the Department of Industrial Engineering at University of Arkansas – Fayetteville. He received a Ph.D. degree in industrial and systems engineering from Rutgers University in 2004. He also earned M.S. degrees in industrial engineering and statistics from Rutgers University, and a B.S. degree in electrical engineering from Beijing Institute of Technology. His research has been sponsored by the U.S. National Science Foundation, Department of Energy, Nuclear Regulatory Commission, Oak Ridge National Laboratory, and industry. The research findings of his group have been published in IISE Transactions, European Journal of Operational Research, Naval Research Logistics, IEEE Transactions on Reliability, IEEE Transactions on Cybernetics, The Engineering Economist, Reliability Engineering & System Safety, etc. In 2014, he served as Chair of INFORMS Quality, Statistics and Reliability (QSR) Section, and President of IISE Quality Control and Reliability Engineering (QCRE) Division. He served as Associate Editor for Journal of Quality Technology and IEEE Transactions on Reliability, and currently serves as Associate Editor for IISE Transactions on Quality and Reliability Engineering. He received the U.S. National Science Foundation CAREER Award in 2010, the IISE QCRE William A.J. Golomski Award for three times, 2013 QCRE Track Best Paper Award, 2015 Stan Ofsthun Best Paper Award, and the prestigious 2017 Alan O. Plait Award for Tutorial Excellence.

Ed Pohl

Ed Pohl is a Professor and Head of the Industrial Engineering Department and holder of the 21st Century Professorship at the University of Arkansas. He has participated and led reliability, risk and supply chain-related research efforts at the University of Arkansas. Before coming to Arkansas, Ed spent 21 years in the United States Air Force where he served in a variety of engineering, operations analysis and academic positions during his career. Ed received his Ph.D. in systems and industrial engineering from the University of Arizona. He holds a M.S. in systems engineering from the Air Force Institute of Technology, and M.S. in reliability engineering from the University of Arizona, an M.S. in engineering management from the University of Dayton, and a B.S. in electrical engineering from Boston University. Ed is the co-editor of the Journal of Engineering Management, an associate editor for the IEEE Transaction on Reliability, the Journal of Risk and Reliability, Journal of Quality Technology and Quantitative Management, and the Journal of Military Operations Research, on the editorial board of the IEEE Transaction on Technology and Engineering Management, and Systems. Ed is a Fellow of IISE, a Fellow of the Society of Reliability Engineers, a Fellow of the American Society of Engineering Management, a Senior Member of IEEE and ASQ, a member of INCOSE, INFORMS, ASEE, MORS and AHRMM.