Survival Analysis-Based System for Predictive Maintenance Optimization

Abstract The adoption of predictive maintenance (PdM) in Industry 4.0 has become essential for optimizing operational efficiency and reducing downtime. Traditional maintenance approaches, such as reactive and preventive maintenance, often result in inefficiencies, highlighting the need for more proactive and data-driven strategies. This paper presents a modular PdM framework that uses survival analysis methods to estimate the remaining useful life (RUL) of industrial components and integrates a Simulated Annealing (SA)-based optimization algorithm to schedule maintenance interventions dynamically. Four survival models—Cox Proportional Hazards (Cox PH), Random Survival Forests (RSF), Gradient Boosting Survival Analysis (GBSA), and Survival Support Vector Machines (Survival SVM)—were evaluated to predict components’ failure, with GBSA emerging as the most robust model due to its resistance to overfitting and ability to capture non-linear degradation patterns. The maintenance scheduling optimization algorithm minimizes downtime by aligning maintenance windows with non-linear degradation/survival functions and production requirements, achieving a 25% reduction in downtime compared to non-optimized methods. The framework was validated on real-world welding electrode degradation data and a synthetic Microsoft Azure PdM dataset, demonstrating its adaptability to heterogeneous industrial environments. Unifying survival analysis with production-aware scheduling optimization enabled cost-effective, risk-informed decision-making for Industry 4.0 applications.