Reliability prediction using a weighted temporal convolutional autoencoder based on limited claim data

Seong-Mok Kim, Min Jung and Yong Soo Kim

Reliability Engineering and System Safety, 2025, vol. 264, issue PB

Abstract: Many product manufacturing companies offer warranty services that cover costs incurred due to product failures during the warranty period. To maximize savings on warranty costs, researchers have attempted to predict field reliability using short-term claim data. Due to the limited information available, however, the prediction performance for long warranty periods has been unsatisfactory. This study proposes a weighted temporal convolutional autoencoder (WTCAE) model designed to predict the number of claims and field reliability over the entire warranty period using limited initial claim data. The WTCAE model compensates for the limited information from initial claim data by effectively capturing temporal patterns through a temporal convolutional network-based encoder–decoder structure. The proposed WTCAE model demonstrated superior performance even under conditions of short-term claim data, where traditional lifetime distribution-based methods fail to provide predictions. It also consistently outperformed conventional deep learning-based methods. The effectiveness and practicality of the proposed WTCAE model were validated using real-world data from millions of televisions and refrigerators, confirming its consistent performance across various data conditions within the warranty period.

Keywords: Autoencoder; Claim; Deep learning; Field reliability; Lifetime distribution; Temporal convolutional network; Warranty (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025005757
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:264:y:2025:i:pb:s0951832025005757

DOI: 10.1016/j.ress.2025.111374

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().