Field-Failure Predictions Based on Failure-Time Data With Dynamic Covariate Information

Abstract

Modern technological developments, such as smart chips, sensors, and wireless networks, have changed many data-collection processes. For example, there are more and more products being produced with automatic data-collecting devices that track how and under which environments the products are being used. Although there is a tremendous amount of dynamic data being collected, there has been little research on using such data to provide more accurate reliability information for products and systems. Motivated by a warranty-prediction application, this article focuses on using failure-time data with dynamic covariate information to make field-failure predictions. We provide a general method for prediction using failure-time data with dynamic covariate information. The dynamic covariate information is incorporated into the failure-time distribution through a cumulative exposure model. We develop a procedure to predict field-failure returns up to a specified future time. This procedure accounts for unit-to-unit variability in the covariate process. We also define a metric to quantify the improvements in prediction accuracy obtained by using dynamic information. We conduct simulations to study the effect of different sources of covariate process variability on predictions. We also provide some discussion of future opportunities for using dynamic data. This article has supplementary material online.

KEY WORDS:

• Accelerated failure-time model
• Cumulative exposure model
• Dynamic data
• Lifetime data
• Usage history
• Warranty returns

ACKNOWLEDGMENTS

We thank the editor, an associate editor, and the referees, who provided valuable comments that helped us improve this article. The work by Hong was supported by the National Science Foundation under Grant CMMI-1068933 to Virginia Tech and the 2011 DuPont Young Professor Grant.