Course Description

This is a hands-on workshop covering important advanced statistical methods and tools for reliability data analysis and test planning. Reliability improvement and reliability assurance processes in manufacturing industries require data-driven reliability information for making business, product-design, and engineering decisions. The course will focus on concepts, examples, models, data analysis, and interpretation.

Course content focuses reliability-based decisions based on the following data sources:

• Life tests.
• Accelerated life tests.
• Accelerated degradation tests.
• Product field and warranty data.

After completing this course, you will be able to:

• Recognize and properly deal with different kinds of reliability data and properly interpret important reliability metrics.
• Use nonparametric estimation to make inferences from censored data with minimal assumptions.
• Use probability plots to identify appropriate parametric models and diagnose anomalies in failure-time data.
• Fit simple distributions to life data, and make inferences on important quantities like distribution quantiles, failure probabilities, and hazard functions.
• Use appropriate methods for computing confidence intervals from censored data.
• Identify and analyze data with multiple failure modes.
• Use regression analysis methods for the analysis of nonnormal-censored data that arise in reliability field studies and in laboratory accelerate life tests.
• Explain the use of physical acceleration models and the dangers involved in the extrapolative inferences frequently required in the field of reliability data analysis.
• Understand the relationship and tradeoffs between traditional failure-time data and degradation data (both destructive and repeated measures degradation data).
• Analyze accelerated destructive degradation data.
• Evaluate the properties of life test and accelerated test plans.
• Conduct comparative studies using reliability data.
• Perform appropriate analyses of recurrence data from repairable systems.

Course Outline

Day 1:

• Appropriate Time Scales for Life Data/Degradation Data.
• Reliability Metrics: Failure Probability, Quantiles, Hazard.
• Simple Nonparametric Estimation.
• Introduction to the SPLIDA Software.
• Weibull/Lognormal Distributions.
• Probability Plots, Detecting Multiple Modes of Failure.
• Parametric Modeling and Fitting Weibull/Lognormal Distributions.
• Life Test Planning.
• Accelerated Repeated Measures Degradation.

Day 2:

• Multiple Failure Modes and Data Analysis.
• Failure Time Regression Analysis.
• Principles of Acceleration Models and Acceleration Factors.
• Accelerated Life Test Data Analysis--One Variable.
• Pitfalls of Accelerated Testing.
• Accelerated Life Tests with More than One Accelerating Variable.
• Planning Accelerated Life Tests.

Day 3:

• Accelerated Destructive Degradation Test Data Analysis.
• Accelerated Repeated Measures Degradation.
• Reliability Comparisons.
• Probability of Correct Selection.
• Analysis of Recurrence Data from Repairable Systems
• Discussion and Illustrations of Software Alternatives.

Course text book:

Statistical Methods for Reliability Data which can be ordered from:
Wiley and Sons:
One Wiley Drive
Somerset, NJ 08875

Ph. 1-800-225-5946
FAX: (906)302-2300

Instructor(s):

Dr. Luis A. Escobar is a Professor in the Department of Experimental Statistics , Louisiana State University. He holds a BS from National University, Medellin, Colombia, an MS from the Inter-American Statistical Training Center (CIENES), Santiago, Chile, and a Ph.D. from Iowa State University. His research and consulting interests include statistical analysis of reliability data, accelerated testing, survival analysis, linear and non-linear models. Professor Escobar is an Associate Editor for Lifetime Data Analysis and past Associate Editor for Technometrics. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Professor Escobar was awarded the 1999 Jack Youden Prize and he has won two awards for outstanding teaching at Louisiana State University. He is the co-author of Statistical Methods for Reliability Data (Wiley 1998), several other book chapters. His publications have appeared in the engineering and statistical literature.

Dr. William Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He holds a BS from Clarkson College of Technology and MS and Ph.D. degrees from Union College. His interests are in the areas of reliability data analysis, statistical methods for quality improvement, statistical planning and inference, and statistical computing. For 15 summers he was summer visitor and consultant at AT&T Bell Laboratories. He worked 3 summers and has been a frequent visitor at the General Electric Corporate Research and Development Center. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Meeker is a former editor of Technometrics and is currently an Associate Editor for Life Time Data Analysis. He has won the American Statistical Association Award for the Best Application paper, the American Society for Quality (ASQ) Wilcoxon Prize three times and also the ASQ Youden Prize four times, and the American Statistical Association Outstanding Application Award. He has won two awards for outstanding teaching at Iowa State University. He is the co-author of two books, including Statistical Methods for Reliability Data (Wiley 1998). He has also written seven book chapters, and of numerous technical publications that have appeared in the engineering and statistical literature.