Implementing OSA-CBM Architecture for Operational Readiness Assessment of Aging Motor Grader Engines - Muhammad Ilham Muslimin; Sepriandi Parningotan; Nova Pangastuti (International Journal of Innovation in Mechanical Engineering and Advanced Materials, 2026)
Traditional interval-based maintenance policies for diesel engines often rely on fixed overhaul schedules recommended by Original Equipment Manufacturers (OEM), which may not accurately reflect the actual health status of aging heavy equipment in harsh mining environments. Such static approaches risk either premature, costly overhauls or catastrophic, unexpected failures. This study proposes a robust methodology by integrating Interquartile Range (IQR)-based statistical control limits within the Open System Architecture for Condition-Based Maintenance (OSA-CBM) framework to evaluate the operational readiness of an aging motor grader engine. Historical oil analysis data from May 2024 to November 2025, covering an extended operational period from 13,515 to 19,755 hours, were examined to identify wear behavior and lubricant degradation. The implementation of the seven functional layers of OSA-CBM, from data acquisition to advisory generation, ensures a structured and traceable diagnostic process. The results indicate that primary wear metal parameters, specifically iron (Fe) and chromium (Cr), remained stable with Risk Index (RI) values below unity (RI < 1), signifying a steady-state wear condition despite the engine operating far beyond typical overhaul intervals. Although a significant isolated spike in aluminum (Al) was detected with a Risk Index of 3.20, the absence of correlated increases in Fe or Cr suggests episodic contamination or a transient event rather than progressive mechanical wear. Furthermore, lubricant condition indicators, including kinematic viscosity and Total Base Number (TBN), consistently complied with SAE 15W-40 specifications. These findings demonstrate that embedding unit-specific statistical boundaries within the OSA-CBM architecture provides reliable, data-driven decision support, enabling justified operational life extension and optimized maintenance strategies for aging heavy-duty engines.