Reliable prediction of industrial components Remaining Useful Life using Cox and Weibull models: A Comparative Study

Predicting the Remaining Useful Life (RUL) of industrial equipment is a cornerstone of predictive maintenance strategies aimed at minimizing downtime and optimizing maintenance costs. This study presents a comparative evaluation of two prominent survival analysis techniques Cox Proportional Hazards (Cox PH) and the Weibull model for RUL prediction using the AI4I 2020 Predictive Maintenance Dataset. We implement a robust analytical framework incorporating Kaplan-Meier survival curves, log-rank tests, and multivariate survival modeling. Our methodology includes detailed data preprocessing, model validation using the C-index and Akaike Information Criterion (AIC), and the identification of significant predictors of failure. The results reveal that the Cox PH model outperforms the Weibull model in terms of flexibility, predictive accuracy, and capacity to handle multiple covariates. This work highlights the strengths and limitations of both models and emphasizes the superior applicability of the Cox PH model for complex industrial datasets. These findings offer actionable insights for developing more reliable, data-driven maintenance strategies in Industry 4.0 environments.