System Reliability Assessment and Simulation by Dr. Rong Pan
Cost $300 per person. Group discounts available 3 hours 9/27/2023
Course Description: System reliability is an important aspect of ensuring the performance and safety of various types of systems. It involves using mathematical models and computer simulations to predict the likelihood of system failure, identify potential weak points, and determine strategies for improving reliability. In this webinar we will discuss several different methods used in system reliability assessment and simulation, including Reliability Block Diagrams (RBDs), Fault Tree Analysis (FTA), and Markov Analysis. Furthermore, Monte Carlo method, a commonly used technique in system simulation, will be explained in detail. This method allows for the incorporation of uncertainty and variability in the system into reliability prediction.
This webinar will provide a brief introduction of software packages such as ReliaSoft BlockSim, JMP Pro and R, and demonstrate their functions of system reliability simulation. BlockSim is specifically designed for RBDs and FTA. It can model complex systems, perform simulations, reliability predictions, and sensitivity analysis to identify the components that have the greatest impact on system reliability. JMP Pro is a powerful data visualization and statistical analysis tool, which can be used to perform various reliability data analysis, and recently it has expanded its range of functions in reliability test planning and system simulation. R is an open-source software with a wide range of packages available for various types of analysis, including system reliability simulation. It is flexible, can handle large data sets and has a large community of users and resources.
Overall, this webinar aims to provide valuable insights into system designs by utilizing simulation tools so that potential reliability issues and the ways to mitigate them can be systematically investigated. Bottom of Form
Instructor: Dr. Rong Pan is an Associate Professor of Industrial Engineering and Data Science in the School of Computing and Augmented Intelligence at Arizona State University. He is the program chair of the Data Science, Analytics and Engineering PhD program at ASU. His research interests include failure time data analysis, design of experiments, multivariate statistical process control, time series analysis, and computational Bayesian methods. He has over 20 years’ experience in teaching and research in the areas of statistical methods for reliability modeling and data analysis. His research has been supported by NSF, Arizona Science Foundation, Air Force Research Lab, etc. He has published over 90 journal papers and 60+ refereed conference papers. He was the recipient of the Stan Ofsthum Award in 2008, 2011 and 2018 and William A. Golomski Award in 2015 and 2020. His papers won the Best Reliability Paper Award of Quality Engineering in 2012 and 2013. Dr. Pan is a senior member of ASQ, IIE and IEEE, and a lifetime member of SRE. He serves on the editorial boards of Journal of Quality Technology and Quality Engineering.
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