Weibull-Based Reliability Scheduling of the Crusher Section in the Lokotrack LT1213S: A Data-Driven Maintenance Framework
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1904-1921
Digital Object Identifier:
10.58578/ajstea.v3i6.7636
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Authors:
Ubbaonu Chinonso Francis Nkeoma1*, Paulinus-Nwamuo Chiedozie Francis2, Alusine Barrie3 
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Abstract
Industrial machinery used in mining and aggregate production is often subject to frequent breakdowns, primarily due to suboptimal maintenance scheduling, resulting in decreased operational efficiency, elevated costs, and safety risks. This study focuses on the Lokotrack LT1213S mobile crushing plant, commonly deployed in quarry operations, with specific emphasis on its crusher section—the most critical and failure-prone subsystem. The research aims to establish an optimal preventive maintenance interval that minimizes unexpected failures while sustaining operational performance. A reliability-based maintenance scheduling framework was developed, utilizing time-to-failure data obtained from field operation logs and analyzed through Weibull statistical modeling in Minitab. Key crusher components—including the belt drive, lining bolts, mill lining, and door bottom—were examined to determine their reliability profiles and failure rates. Reliability plots and probability density functions were employed to predict component degradation and expected lifespans. The analysis revealed that the crusher subsystem maintains a reliability above 90% during the initial 24 to 31 operating hours, beyond which failure probability increases significantly. Consequently, a preventive maintenance interval within this range is recommended to optimize equipment uptime and reduce unplanned downtime. These findings align with established reliability engineering methodologies, particularly those proposed by Ebeling [1], Villanueva et al. [2], and Chen & Zhang [6]. The study concludes that Weibull-based reliability analysis provides a robust foundation for predictive maintenance planning, contributing to improved safety, prolonged equipment lifespan, and enhanced productivity in mining operations.
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