用生存分析预测喷头寿命，提升工业维护精度。
Predicting the Lifespan of Industrial Printheads with Survival Analysis
- 采用五种生存分析方法建模喷头失效概率。
- 相比行业基准，预测误差降低23%以上。
- 适合设备运维、智能制造领域研究人员。
准确预测关键设备部件的使用寿命对维护规划和生产优化至关重要，是学术界与工业界共同关注的重要课题。本文研究了生存分析在佳能生产打印公司开发的印刷喷头寿命预测中的应用，重点评估了五种技术：Kaplan-Meier估计器、Cox比例风险模型、Weibull加速失效时间模型、随机生存森林与梯度提升。通过等熵回归进一步优化结果，并聚合得到预期故障数量。模型在多个时间窗口下与真实数据对比验证，使用三项性能指标进行量化评估，结果表明生存分析显著优于行业标准基线方法。
原文摘要 · Abstract (English)
Accurately predicting the lifespan of critical device components is essential for maintenance planning and production optimization, making it a topic of significant interest in both academia and industry. In this work, we investigate the use of survival analysis for predicting the lifespan of production printheads developed by Canon Production Printing. Specifically, we focus on the application of five techniques to estimate survival probabilities and failure rates: the Kaplan-Meier estimator, Cox proportional hazard model, Weibull accelerated failure time model, random survival forest, and gradient boosting. The resulting estimates are further refined using isotonic regression and subsequently aggregated to determine the expected number of failures. The predictions are then validated against real-world ground truth data across multiple time windows to assess model reliability. Our quantitative evaluation using three performance metrics demonstrates that survival analysis outperforms industry-standard baseline methods for printhead lifespan prediction.
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