In industrial applications, reliability is crucial and testing is expensive. Collected data must be exploited in the best way possible. Reliability data possess specific features that call for dedicated statistical methods. Learn about statistical tools for reliability analysis.
Statistical Tools for Reliability Studies
Upon completion of this module, participants will be able :
- To understand the motivation and the principle underlying statistical tools for life data analysis
- To understand the notion of competing risks, censoring and their impact on the results
- To know that the statistical techniques adapted to life data are available
- To use tools to build and to compare survival curves
- To interpret results and what the scope of the results are
- To know the scope and limitations of the various approaches available
This module is intended for engineers who want to improve the way to design and analyse reliability studies with the increased knowledge of the most recent tools in this field. It is also intended for people who need to evaluate the scope and validity of reliability studies.
This module introduces the important ideas in statistics and data analysis applied to the field of shelf-life and stability studies. It assumes that participants have no previous knowledge of statistics or that they have not used such notions in a long time.
- General Introduction to Reliability and Life Data
- What is Reliability?
- From Simple to More Complete Cases
- Origins of Life Data
- A Widening Range of Applications
- Specific Data Acquisition Issues
- Typical Data Layout
- The Need to Use Adapted Statistical Data Analysis Techniques
- Life Data and the Normal Distribution
- Other Modeling Issues
- Data Analysis of Failure Time Data
- Modeling Survival Curves
- General Statistical Concepts
- Non-Parametric Approach
- Parametric Approach
- Regression Models: Cox and Alternatives
- Semi-Parametric Models
- Accelerated Life Models: Principle and Limitations
- Summary and Conclusion
Recommended Course Duration: 1 day
Course materials include course notes on the statistical techniques covered in the module as well as sample datasets.