Reliability Analysis

Gain fundamental understanding of the concepts and methods needed to plan, collect and analyze data for actionable results. The course focuses on the proper application of statistical methods to model failure data, estimate quantities of interest for both components and systems, and make predictions. Knowledge of basic algebra is helpful.

Seminar Content
Quality Statistics Review
Understanding variation
Process Stability and Capability
Measurment uncertainty

Basic Reliability Concepts
Definition of Terms
Four conceptual models of failure
Reliability and maintainability
Achieving reliability by design

Foundations of reliability statistics
Fundamental rules of probability
A first lesson in Weibull analysis
Continuous probability functions
Reiliability, failure and hazard functions
Parameter estimation for figures of merit

Confidence intervals for reliability statistics
Three types of statistical intervals
Confidence intervals for MTBF
Nonparametric test designs
Binomial confidence intervals
Chi-square approximation for Poisson confidence intervals

Graphical techniques for Weibull analysis
Brief history of Weibull distribution
Weibull probability paper
Two-parameter Weibull analysis
Median rank values
Projections of B10 life
Implication of the shape parameter
Hidden Weibull failure modes

Reliability models for systems
Two reliability strategies
Reliability block diagrams
Reliability allocation models
Reliability prediction tools
Reliability testing

Using dirty data
Uncertainty with small samples
Suspensions and censored data
Analysis of curved Weibull plots
Inconsistent data/multibple failures
Planning for future data management

Advanced concepts in Weibull analysis
Analysis of grouped failure data
Variables used in failure forecasting
Confidence intervals for Weibull plots
Weibull data in various test strategies

To request a quote, contact:
Elena O'Connor, On-site Programs
Phone: (734) 487-4926