Service Life Reliability Predictions for Heat Exchangers: A 9 years-Warranty Data Analysis

Extended automotive warranty mandates present a critical challenge for Tier-1 suppliers who must navigate sparse downstream visibility and non-stationary field failure rates. This paper proposes a robust statistical framework for long-term warranty risk assessment, specifically addressing the data-gap between manufacturer shipments and vehicle-in-operation (VIO) exposure. We introduce a multi-stage methodology that integrates: (i) an exposurealignment algorithm utilizing shipment-suspension synchronization, (ii) a clustering technique based on Parts Per Million (PPM) trend analysis to mitigate quality heterogeneity, (iii) a composite reliability modeling approach employing Weibull and Mixed-Weibull distributions.
The proposed approach is validated through an industrial case study involving automotive components, utilizing 0− 6 years of matured warranty data and 7th-8th year immature claims to forecast warranty performance over an extended horizon from 3 to 15 years. The forecasted warranty returns demonstrate strong agreement with observed field data, with actual claims consistently falling within the 90 % upper confidence limits. Incorporation of vehicle survivability via a scrappage-rate adjustment further enhances long-term forecast realism. The results confirm that the proposed framework effectively addresses industrial data constraints and provides a robust, practical solution for extended warranty provisioning and risk management in automotive applications.

Keywords: Extended Warranty, Mixed-Weibull, Warranty Risk Assessment, Scrappage-rate, PPM.