The Impact of Wear on Risk Assessment and Preventive Maintenance Optimization for Mining Excavators: A Systematic Review

This research presents an integrated, multidisciplinary framework for transitioning from traditional preventive maintenance to predictive (PdM) and risk-based maintenance (RBM) strategies applied to heavy excavation machinery (Liebherr R9250 and R9350 fleets). The study merge’s reliability engineering principles with advanced Industry 4.0 technologies—including Digital Twin architectures, Stacking Ensemble Machine Learning algorithms, and IoT sensor networks—to maximize Mechanical Availability (MA ≥ 85%) and Overall Equipment Effectiveness (OEE ≥ 75%). The study evaluates these performance targets as standard benchmarks synthesized from state-of-the-art literature for optimized fleets, rather than as values derived from a single isolated field experiment. Special emphasis is placed on the direct correlation between mechanical degradation (wear of Ground-Engaging Tools—G.E.T. and hydraulic systems) and ergo-physical impacts on operators (structural vibrations and elevated acoustic emissions), evaluated using the HFACS framework and biometric data fusion. From a financial perspective, mathematical modeling of cumulative cost functions (Total Cost of Ownership—TCO) demonstrates a break-even point at t = 1.428,57 operating hours and a net maintenance cost reduction of 18–22% over a 5000 h operational cycle. Furthermore, the implementation of Fault Tree Analysis (FTA) and 5 × 5 risk matrices confirms up to a 66% risk score reduction for critical failure modes. The synthesized outcomes validate the practical execution framework designed to support the ‘Triple Zero’ strategic paradigm (zero accidents, zero unplanned downtime, zero environmental compromise) as a long-term management vision and progressive operational objective, offering a sustainable decision-making model for surface mining operations.