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Advanced Methods for Reliability Analysis 3-Day Course
Course Description
This course is intended to provide deeper levels of knowledge of reliability and risk analysis methods to those individuals who have already been exposed to introductory courses in the field. The topics are selected from a cross section of advanced methods and techniques developed in recent years with immediate and significant practical application. The target audience is Reliability Engineers, Risk Analysts, and Systems Design Engineers.Who Should Take the Course
This course will benefit Reliability Engineers, Risk Analysts, and Systems Design Engineers.What the Student Will Learn
The student will develop a deeper understanding of advanced tools and techniques for developing more realistic reliability models of complex systems, and use of limited data and information to estimate the pentameters of such modes. Examples drawn form real applications will enable the students to develop necessary skills for dealing with problems encountered in reliability and risk analysis of highly reliable systems.Included Materials
Class notes and presentation slides.Required Materials Scientific calculator. Laptop computer recommended.
Recommended: "Reliability Engineering and Risk Assessment," E.J. Henely and H. Kumamoto, Prentice-Hall, IncDr. Ali Mosleh, Ph.D.
Dr. Mosleh is Professor and Director of the Reliability Engineering Program at the University of Maryland and Director of the Center for Risk and Reliability. Dr. Mosleh has made many contributions to diverse fields of theory and applications including methods for reliability data classification and analysis, Bayesian inference methods, inference with uncertain evidence, risk and reliability of hybrid systems of hardware, human and software elements, human reliability assessment, particularly cognitive models of man-machine interactions, space systems probabilistic risk analysis, nuclear power risk methods and applications, and information security risk analysis. He is the developer of the Accident Precursor Analysis methodology, methods for treatment of common cause failures, models for assessing the influence of organizational factors on system safety, and methodology for elicitation and use of expert judgment in systems reliability and risk analysis. He has authored more than 200 technical articles and books, holds several patents, and has led the development team for many large scale software tools for reliability data analysis, systems analysis, and risk assessment of complex systems. Dr. Mosleh is a Fellow of the Society for Risk Analysis (SRA), has chaired or organized numerous technical conferences on reliability and risk assessment, has been awarded several scientific achievement awards, and is a consultant and technical advisor to many national and international organizations. Dr Mosleh holds a PhD in Nuclear Science and Engineering from UCLA.
Reza Azarkhail
Dr. Azarkhail is a research associate in the center for Reliability and Risk at University of Maryland, and his research areas are Reliability and Risk assessment of complex systems, Importance measures of risk, Intelligent agent-Based approach for direct simulation of reliability and risk in complex systems, Physics of Failure based component reliability assessment, Bayesian Inference using Markov Chain Monte Carlo simulation, Accelerated Life Testing and Reliability of Consumer products.
Dr. Azarkhail currently holds a bachelor and master's degrees in Mechanical Engineering at Thermal and Fluids & Energy Conversion from Sharif University of Technology. He has also received his master's and Ph.D. in Reliability Engineering from University of Maryland at College Park.
Course Outline
Day 1- Mathematical Theory of System Models
- Algorithms for Analysis of Complex Systems Models
- Dependent Failures Modeling and Analysis
- Dynamic Systems Reliability (including Markov Models and simulation approaches)
- Repairable Systems Reliability, Ordinary and Generalized Renewal Theory
- Reliability Growth
- Bayesian Methods in Reliability Analysis
- Uncertainty Analysis and Propagation
- Human Reliability Analysis
- Software Reliability Analysis

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