RIAC

Click here for the RIAC Training page

Collection and Analysis of Reliability Data 1-Day Course

Course Description

The course focuses on the collection and analysis of reliability data to make informed decisions. Topics covered include beginner to advance topics in the collection and analysis of reliability data.

Who Should Take the Course

Anyone interested in or involved with the collection and analysis of reliability data used to make informed decisions. This includes design and manufacturing engineers, production personnel, maintenance technicians, maintenance engineers, reliability engineers, test engineers, and maintenance and production management.

What the Student Will Learn

Basic concept in variation, probability concepts, statistical distributions, reliability prediction and modeling methods, mathematical techniques used to analyze and solve reliability problems, reliability data collection and analysis, accelerated life models, and repair systems modeling.

Dr. Ali Mosleh

Professor Modarres is the Director of the University of Maryland's Center for Reliability Engineering the world leader in reliability education. He has authored numerous books in the fields of reliability and risk management, most recently "Risk Analysis in Engineering: Probabilistic Techniques, Tools and Trends." Dr. Modarres has an MS in Mechanical Engineering and a PhD in Nuclear Engineering from MIT and his current research interests are Probabilistic Risk Assessment, Complex Systems Functional Modeling, and Deterministic-Probabilistic Modeling of Engineering Systems. Dr. Modarres is a former University of Maryland Distinguished Scholar-Teacher and a Maryland Inventors' Award Winner. He has supported dozens of government and industry organizations in the fields of reliability and risk assessment.

Course Outline

Understanding of variation
Probability concepts
Rules of probability
Probability distributions
Discrete distributions
Continuous distributions
Statistical confidence
Statistical hypothesis testing
Non-parametric inferential methods
Goodness of fit
Series of events (point processes)
Computer software for statistics
Fault tree analysis
Reliability block diagrams
Failure mode and effects analysis (FMEA)
Event tree construction and evaluation
Reliability data collection and analysis
Methods of modeling systems for reliability analysis
Mathematical techniques used to analyze and solve reliability problems
Matrices, vectors, tensors, differential equations, integral transforms, and probability methods used to solve reliability problems
Probabilistic life models for components with both time independent and time dependent loads
Data analysis, parametric and nonparametric estimation of basic time-to-failure distributions
Data analysis for systems
Accelerated life models
Repairable systems modeling