End-of-line (EoL) testing is performed to determine product quality by ensuring reliable performance. Even though low-quality products may pass EoL testing, they have a high probability of failure over time. Analyzing product usage data can help to improve EoL testing in this regard. However, current approaches do not consider usage data for this purpose. The major challenge for manufacturers is that they do not have access to comprehensive usage data for their products because customers are unwilling to provide usage data. However, manufacturers obtain some usage data from their sales and service departments i.e., contextual data. In this paper, we introduce an alternative approach to improving EoL testing when usage data from customers are missing. We discuss whether it is possible to predict low-quality products from EoL testing data when only contextual information is available (i.e., historical service data and location data of shipped products). We find that a simple, duration-based product usage threshold is sufficient to separate products affected by the production process (low-quality products) from those affected primarily by usage and environmental factors (long-term influence). Low-quality products could only be predicted by combining EoL data and contextual data. Additionally, we identify frequent patterns of maintained components to tackle the challenge of having limited data and promote user acceptance of our predictive model. Finally, we demonstrate our approach by conducting a case study in the welding industry. Our approach can identify frequent component failures and improve product reliability in countries with varying environmental conditions and rates of product usage. We expect that our findings will improve EoL testing protocols in welding and other industries while improving defect prediction models in general.

执行下线 (EoL) 测试以通过确保可靠的性能来确定产品质量。尽管低质量的产品可能通过了 EoL 测试，但随着时间的推移，它们很有可能出现故障。分析产品使用数据有助于改进这方面的 EoL 测试。然而，当前的方法并未考虑用于此目的的使用数据。制造商面临的主要挑战是他们无法访问其产品的全面使用数据，因为客户不愿意提供使用数据。然而，制造商从他们的销售和服务部门获得一些使用数据，即上下文数据。在本文中，我们介绍了一种在客户使用数据缺失时改进 EoL 测试的替代方法。我们讨论了当只有上下文信息（即历史服务数据和发货产品的位置数据）可用时，是否可以从 EoL 测试数据中预测低质量产品。我们发现一个简单的、基于持续时间的产品使用阈值足以将受生产过程影响的产品（低质量产品）与主要受使用和环境因素影响（长期影响）的产品区分开来。低质量的产品只能通过结合 EoL 数据和上下文数据来预测。此外，我们确定了维护组件的频繁模式，以应对数据有限的挑战并促进用户对我们预测模型的接受。最后，我们通过在焊接行业进行案例研究来展示我们的方法。我们的方法可以在环境条件和产品使用率不同的国家/地区识别频繁的组件故障并提高产品可靠性。我们希望我们的发现将改进焊接和其他行业的 EoL 测试协议，同时改进一般的缺陷预测模型。