Hydraulic Oil Splitter - Wholesale & Manufacturers

Contamination Removal Efficiency Over Operating Hours

This line chart illustrates the relationship between operating hours and hydraulic oil splitter performance, measured by removal efficiency (%) and relative contaminant index (RCI). The horizontal axis represents cumulative operating hours (0–2000 h) while the primary vertical axis shows removal efficiency as a percentage. Two series are plotted: 'Removal Efficiency (%)' and 'Relative Contaminant Index (0–100)'. The Removal Efficiency series starts high near 98% at 0 hours, dips gradually as filters load and wear increase, and then drops more sharply beyond 1200 hours, reflecting reduced separation performance. The Relative Contaminant Index is an inverse indicator scaled 0–100 where higher values mean more contaminants present; it starts at 10, rises slowly during early operation, accelerates after 800 hours, and peaks near 85 at 2000 hours. Together the trends demonstrate an expected trade-off: as contaminant accumulation increases, removal efficiency declines. Early-life stability suggests effective initial separation and fresh filter media. The inflection point around 800–1200 hours indicates the onset of accelerated performance degradation, which can guide maintenance planning. For instance, scheduling filter inspections or replacements before the steep efficiency drop can maintain peak performance and extend system life. Operators can use the RCI threshold (e.g., 50) as a proactive alarm to service equipment rather than waiting for efficiency to fall below acceptable levels. This visualization supports decisions for preventive maintenance, spare parts inventory, and lifecycle cost modeling. It also highlights the value of monitoring both efficiency metrics and contaminant trends rather than relying on a single reading. By combining these indicators, maintenance teams can better predict optimal service intervals, reduce unplanned downtime, and ensure hydraulic systems continue to meet performance requirements. Regularly capturing similar datasets and updating the model with operating conditions, fluid properties, and ambient temperatures will refine predictive maintenance algorithms and further minimize lifecycle costs while maximizing system reliability performance.