Predictive Reliability Modelling and Maintenance Optimization of Belt Conveyor Systems in Underground Coal Mines

Belt conveyor systems are vital for efficiency and safety in underground coal mining but remain vulnerable to frequent failures stemming from wear, fatigue, and component misalignment. Using a this dataset comprising breakdown records, time before failure, frequency, repair duration, and operational hour this study applies predictive reliability modelling with Weibull distribution analysis to assess the performance of key components such as belting, motors, gearboxes, rollers, drums, couplings, and structural elements. Reliability parameters including the shape factor (β), scale factor (η), and Mean Time Between Failures (MTBF) were estimated, and preventive maintenance intervals were determined at target reliability thresholds (R* = 0.90, 0.80, 0.70). The analysis shows that predictive maintenance, when scheduled from data set, can reduce downtime by 22–28%, extend MTBF by 15–20%, and considerably improve both safety and system availability. The study demonstrates that incorporating datasets into condition-based monitoring and reliability modelling offers a scalable framework for optimizing maintenance strategies, minimizing costs, and enhancing the dependability of conveyor systems in underground coal mines.