Predicting the Lifespan of Industrial Printheads with Survival Analysis
This work addresses maintenance planning and production optimization for industrial equipment, but it is incremental as it applies existing survival analysis methods to a specific domain.
The paper tackled predicting the lifespan of industrial printheads using survival analysis techniques, and the result showed that this approach outperformed industry-standard baseline methods in quantitative evaluations.
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.