RIAC

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Human Reliability Analysis 3-Day Course

Course Description

The course focuses on techniques of human error rate prediction, human performance shaping factors, human machine systems analysis, skill levels, source of human error probability data and includes examples and case studies. Topics covered will assist in design for Reliability and Maintainability for both new systems or systems to be improved.

It is expected that participants will have a basic understanding of statistics, probability and the processes that lead to failures in order to best appreciate the Human Reliability course. Some of the topics which will be quickly reviewed at the beginning of the course are:

Probabilistic reliability
Load and strength
Repairable and non-repairable items
Pattern of failures with time (repairable and non-repairable)
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)

In addition, an understanding of methods of reliability analysis is useful to best appreciate the Human Reliability course. These methods include:

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

Who Should Take the Course

Persons involved in the design or use of equipment with which humans interface, reliability, maintenance and manufacturing engineers, maintenance technicians, planners and schedulers, maintenance and production management, risk managers, safety professionals, and people providing services requiring human interaction.

What the Student Will Learn

Consideration of reliability in the design, manufacturing/planning and maintenance of products and services, the use of probability and statistics to specify and determine the reliability of services and products, the processes that lead to failures, principal methods of reliability analysis, reliability data collection and analysis, methods for solving practical human reliability problems, techniques for human error rate prediction, human performance shaping factors, human machine systems analysis, human skills level and sources of human error probability data.

Dr. Marvin Roush, Ph.D.

Dr. Roush received his Ph.D. from the University of Maryland in 1964. Vice-President, IEEE Reliability Society; Member; Editorial Board, Reliability Engineering and System Safety Journal; Advisory Committee of the IEEE Reliability Society, IEEE Reliability Society Education Committee; Member, The Aerospace Industries Association Taskforce on Ultra-Reliable Electronic Systems, The ASQC Reliability Division Scholarship Committee, The Working Group 12 on Risk Assessment of the United States Committee of the International Electro-technical Commission and Member-at-Large of the Management Committee of ANSI ASC Z1, which is the organization designated to coordinate development of U.S. standards on quality assurance, reliability and statistics. Consultant to NASA's Goddard Space Flight Center.

Course Outline

Source of human error probability data
Methods of solving practical human reliability problems
Technique of human error rate prediction (THERP), SLIM, OAT and SHARP methods
Human performance shaping factors
Human machine systems analysis
Distribution of human performance. and uncertainty bounds
Human skill levels
Examples and case studies