Analysis of Non-Homogeneous Warranty Data

When performing reliability analysis on warranty returns data, it is important to differentiate between cases where the data can be considered to be homogeneous and cases where the data are non-homogeneous. In the case of homogeneous data, all sold and returned units are considered to be exactly the same, i.e. the same population with no design changes and/or modifications. In many situations, however, the units being sold might not all be considered the same due to differences in design, material, etc. For example, many times design changes are made to enhance and/or improve the reliability of the product as the product matures.

In this article, we will explore how non-homogeneous warranty data can be analyzed and how predictions can be made from such data. To analyze such cases, one needs to extract each homogeneous group, fit a life model to each group and then project the expected returns for each group based on the number of units at risk for each specific group.

Parametric Recurrent Event Data Analysis and the GRP Model

Issue 57 introduced the concept of recurrent event data and presented a non-parametric approach to analyze such data. Weibull++ 7 also offers a parametric approach that is performed using the Parametric RDA Specialized Folio. This type of folio can capture the trend, estimate the rate and predict the total number of recurrences. The failure and repair data of a repairable system can be treated as one type of recurrent event data. Past and current repairs may affect the future failure process. For most recurrent events, time (distance, cycles, etc.) is a key factor. With the time, the recurrence rate may keep constant, increase or decrease. For other recurrent events, not only the time, but also the number of events can affect the recurrence process, e.g. the debugging process in software development.

The parametric analysis approach utilizes the General Renewal Process (GRP) model. In this model, the repair time is assumed to be negligible so that the processes can be viewed as point processes. This model provides a way to describe the rate of occurrence of events over time such as in the case of data obtained from a repairable system. This model is particularly useful in modeling the failure behavior of a specific system and understanding the effects of the repairs on the age of that system.