Introduction to Life Data Analysis

Reliability Life Data Analysis refers to the study and modeling of observed product lives. Life data can be lifetimes of products in the marketplace, such as the time the product operated successfully or the time the product operated before it failed. These lifetimes can be measured in hours, miles, cycles-to-failure, stress cycles or any other metric with which the life or exposure of a product can be measured. All such data of product lifetimes can be encompassed in the term life data or, more specifically, product life data. The subsequent analysis and prediction are described as life data analysis. For the purpose of this reference, we will limit our examples and discussions to lifetimes of inanimate objects, such as equipment, components and systems as they apply to reliability engineering, however the same concepts can be applied in other areas.

An Overview of Basic Concepts

When performing life data analysis (also commonly referred to as Weibull analysis), the practitioner attempts to make predictions about the life of all products in the population by fitting a statistical distribution (model) to life data from a representative sample of units. The parameterized distribution for the data set can then be used to estimate important life characteristics of the product such as reliability or probability of failure at a specific time, the mean life and the failure rate. Life data analysis requires the practitioner to:

1. Gather life data for the product.
2. Select a lifetime distribution that will fit the data and model the life of the product.
3. Estimate the parameters that will fit the distribution to the data.
4. Generate plots and results that estimate the life characteristics of the product, such as the reliability or mean life.