Applied Statistics for Reliability (RQ380)

SynopsisStatistics is the basic for reliability test data analysis to predict product lifetime prediction. Coupled with other methods, the dominant failure mechanisms can then be identified. The reliability analysis typically requires more advanced statistics as compared to the statistics used in quality. Therefore, a detailed understanding of statistics and their application is critical. This course is also designed to meet the certified reliability engineer examination.

The course will cover the essential elements of statistics used in reliability data analysis, and it also serves as a refresher for practicing reliability engineers.

What You Will Learn

• Basic probability concepts
• Probability distribution
• Statistical process control
• Statistical inference
• Design of experiment

Who Should Attend

• Process engineers
• QA engineers
• Product engineers

PrerequisiteBasic statistical and probability theory.

Course MethodologyThis course is presented classroom style, with examples to illustrate the concepts taught.

Course Duration5 days, 9am - 5pm

Course Structure1) Introduction

• What is reliability
• Different types of reliability tests and their purposes
• Different types of reliability test data
• Why statistics is important in reliability data analysis
• Differences of statistics used in quality and reliability
• Common mis-use of statistics in reliability and its consequence

• Definition of statistical terms and their applications in quality and reliability
• Define the basic probability concepts such as independence, mutually exclusive, conditional probability, etc., and the computation of expected values

• Describe and distinguish between various distributions (binomial, Poisson, exponential, Weibull, normal, log-normal etc) and their functions such as CDF, pdf, etc.
• Application of the distributions in quality and reliability, including statistical process control
• Describe how SPC is related to reliability

• Point and interval estimates of distribution parameters
• non-parametric and parametric estimation
• probability plotting
• confidence interval
• hypothesis testing
• Bayesian technique
• design of experiments