IEEE Transactions on Reliability

Special Section on Dynamic Reliability Analysis
of Complex Systems


Call for Papers


Background


Dynamic reliability analysis of complex systems has become very challenging due to factors such as uncertainties associated with their properties, operation environments, nonlinearities, and stochastic behavior. Although solving the dynamic equations of complex systems including nonlinearities and uncertainties has been addressed in other studies, it is still of great interest in academia and industry.


This special section will focus on dynamic and time-varying reliability estimation and prediction for complex systems, numerical methods for dynamic reliability analysis, reliability prediction based on limited experimental data and stochastic modeling of complex systems.

Topics


The topics of interest include, but are not limited to, the following work on complex systems


  • Innovative methodologies for stochastic modeling
  • Reliability analysis, estimation, and prediction considering nonlinearities, stochastic behavior, and uncertain operation conditions
  • Methods of design for lifetime reliability
  • Methods in reliability analysis with limited experimental data
  • Reliability analysis using machine learning
  • Maintenance scheduling and predictive maintenance
  • Methods for design and interpretation of accelerated life tests
  • Case studies and tool support

Submission Information


We welcome high quality submissions that are original work, not published, and not currently submitted elsewhere. We also encourage extensions to conference papers, unless prohibited by copyright, if there is a significant difference in technical content. Improvements such as adding a new case study or including a description of additional related studies do not satisfy this requirement. A description explaining the differences between the conference paper and the journal submission is required. The overlap between each submission and other articles, including the authors’ own papers and dissertations, should be less than 30%.


All submitted papers will undergo a rigorous peer-review process and must conform to the double column, single-spaced format of printed articles in the IEEE Transactions on Reliability with all figures and tables embedded in the paper, rather than listed at the end or in the appendix. Refer to the special guidelines posted at https://mc.manuscriptcentral.com/tr-ieee.

Important Dates


  • December 31 15, 2020
  • April 1, 2021
  • April 15, 2021
  • Paper submission deadline
  • All reviews back
  • First round notification

Editor-in-Chief


  • Professor W. Eric Wong, University of Texas at Dallas, USA

Guest Editors


About the Guest Editors


Professor Ming J Zuo received the Bachelor of Science degree in Agricultural Engineering in 1982 from Shandong Institute of Technology, China, and the Master of Science degree in 1986 and the Ph.D. degree in 1989 both in Industrial Engineering from Iowa State University, U.S.A. He is currently a Full Professor in the Department of Mechanical Engineering at the University of Alberta, Canada. His research interests include system reliability analysis, maintenance modeling and optimization, signal processing, and fault diagnosis. He is an Associate Editor of IEEE Transactions on Reliability, Department Editor of IIE Transactions (2005-2008, 2011-present), Regional Editor for North and South American region for International Journal of Strategic Engineering Asset Management, and Editorial Board Member of Reliability Engineering and System Safety, Journal of Traffic and Transportation Engineering, International Journal of Quality, Reliability and Safety Engineering, and International Journal of Performability Engineering. He is a Fellow of the Institute of Industrial Engineers (IIE), Fellow of the Engineering Institute of Canada (EIC), Founding Fellow of the International Society of Engineering Asset Management (ISEAM), and Senior Member of IEEE.




Professor Zhaojun (Steven) Li is an Associate Professor at the Department of Industrial Engineering and Engineering Management, Western New England University in Springfield, MA. Professor Li’s research interests focus on Reliability, Quality, and Safety Engineering in Product Design, Systems Engineering and Its Applications in New Product Development, Diagnostics and Prognostics of Complex Engineered Systems, and Engineering Management. He earned his doctorate in Industrial Engineering from the University of Washington in 2011. He is an ASQ certified Reliability Engineer, and Caterpillar Six Sigma Black Belt. Professor Li has publications in many journals including IIE Transactions on Quality and Reliability, Reliability Engineering and System Safety, Journal of Manufacturing Systems, IEEE Transactions on Reliability, and Quality Engineering. His most recent industry position was a reliability team leader with Caterpillar Rail Division to support the company’s Tier 4 Locomotive New Four Stroke Engine and Gas-Diesel Dual Fuel Engine Development.




Professor Xihui (Larry) Liang is an Assistant Professor in the Department of Mechanical Engineering, University of Manitoba, Canada. He received his Bachelor’s and Master’s degrees in Mechanical Engineering from Shandong University, China, in 2007 and 2009, respectively, and his PhD degree in Mechanical Engineering from the University of Alberta, Canda, in 2016. After that, he worked as a Postdoc Research Fellow at the University of Alberta for about two years. His specialty areas mainly include condition monitoring, reliability analysis, predictive maintenance and intelligent manufacturing. He has authored/coauthored more than 40 articles in prestigious journals, such as IEEE Transactions on Industrial Electronics, Mechanical Systems and Signal Processing and Reliability Engineering & System Safety.




Dr. Sajad Saraygord Afshari received his PhD degree from the Sharif University of Technology, Iran, with a focus on stochastic dynamics and reliability-based control of structures. His PhD thesis title is “Active Reliability Based Failure Prediction and Control of Wing Structures via Online SHM Using Hybrid Algorithms and Experimental Data” He is experienced in numerical simulation, control engineering, experimental mechanics, reliability analysis, and machine learning techniques. He is currently working as a postdoctoral fellow at the Intelligent Monitoring and Control Laboratory in the Department of Mechanical Engineering at the University of Manitoba. His current mission is the development of probabilistic and stochastic machine learning methods for improving design, manufacturing, and performance of industrial products.


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