Optimizing Condition Based Maintenance for Assessing Failure Indicators in Heavy Equipment Components

Abstract

The coal industry remains attractive business, making heavy equipment quite essential for efficient coal mining operations. Effective maintenance of this equipment is crucial for maintaining performance, as breakdowns and component failures can lead to significant losses and disrupt mining activities. To mitigate these risks, this study focuses on optimizing Condition-Based Maintenance (CBM) to better identify and reduce component failures. To optimize CBM, especially in assessing component health, an index has been developed to represent component condition. Fifteen parameters are used to determine the health of Komatsu HD785-7 engine components. Each parameter plays a distinct role and carries a different weight in identifying failure indicators for the components. The determination of these weights requires operational analysis approach to achieve optimal values. The index used to represent engine component health is called the Condition Monitoring Index (CMI). Through rigorous monitoring and evaluation, the incidence of unscheduled overhauls can be significantly reduced. The CMI can serve as a guide for determining subsequent proactive maintenance actions. Continuous monitoring and evaluation are essential for detecting early engine component failures.