Duration: 3 days
Introduction:
Reliability has been involved deeply in the everyday lives of human beings and has gained increasing attention in many industries. JMP has dedicated for many years to develop a comprehensive suite of platforms for reliability data analysis and reliability engineering. The suite supports not only mature and traditional methodologies, but also leads the development in the research forefront. This course is to teach the audience how to analyze product life and accelerated testing.
Purposes:
This course will cover the statistical methods used in the reliability analysis and provide hands-on practice of using software. During the course, the trainees will gain a comprehensive and deep understanding about statistical tools for various reliability problems and learn how to use JMP to analyze them. This course aims to equip trainees with ready-to-use skills for their daily tasks, enabling them to provide valuable inputs to management and business decisions.
Who Should Attend:
This course is designed for those whose work is related to product reliability, including students, faculty members, design or test engineers, managers, and consultants. This course will cover basic statical theories, complex reliability concepts, and detailed guide to how to use the software that are all essential for accomplishing common or sophisticated reliability tasks. Trainees with different backgrounds may benefit from different aspects of the course. Trainees who attend this course should already be familiar with the basic use of JMP software, which includes understanding the structure of JMP data table, the concept of JMP platform, and the relationship between data column and variables in platform launch dialog. For statistical background, trainees must have a basic understanding of statistical distribution and linear regression.
Content:
• Day 1
o Basis of reliability analysis
o Types of reliability data and metrics of interest
o Nonparametric estimates and confidence intervals
o Weibull and lognormal distributions
o Definition and applications of probability plot
o Maximum likelihood inference, parametric estimates, and confidence intervals
o Parametric model selection
o Bayesian statistical methods for reliability
o Failure modes and analysis
o Warranty analysis (reliability forest)
o Reliability demonstration and reliability test plan
• Day 2
o Analysis of accelerated life-test (ALT) data
o Overview of ALT methods
o Physics-based acceleration models
o Temperature-accelerated life tests
o ALT with two accelerating variables
o Design of ALT
o Varying-stress and step-stress models
o Degradation modeling
o Accelerated repeated measures degradation
o Bayesian analysis of accelerated repeated measures degradation
o Accelerated destructive degradation.
o Fatigue Model（JMP 18 New Feature）
o Overview of Fatigue Model (also known as S-N curve modeling)
o Introduction of Fatigue Models
o Fatigue data analysis
• Day 3
o Reliability analysis of non-repairable systems
o Introduction to reliability block diagram (RBD)
o Metrics of system reliability
o Components of RBD and configuration settings
o Analysis of system reliability
o RBD based reliability allocation
o Reliability analysis of repairable systems
o Introduction to repairable systems simulation (RSS)
o Components, events, and actions in RSS
o RSS based outage time analysis
o RSS based budget planning
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