Monte Carlo–based Reliability and Performance Assessment of Mobile Air and Natural Gas Lift Compressors

Abstract

This study presents a comprehensive reliability assessment and operational performance evaluation of mobile air compressor systems and G-3516B Altra lean burn gas lift compressors using Monte Carlo simulation. For the mobile compressors, operational and maintenance records from 2021 to 2024 were analyzed for four key components, namely valves, filters, radiators, and air hoses, to estimate reliability indices, including mean time between failure (MTBF), failure rate, reliability, availability, and unavailability. The results reveal a progressive degradation in component performance throughout the study period. Valve reliability declined sharply from 13.1% in 2021 to 2.3% in 2024, accompanied by a reduction in MTBF from 496 to 168 hours. Filters exhibited the poorest performance, with reliability decreasing from 6.6% to 1.7% and MTBF falling from 372 to 156.8 hours, indicating a high susceptibility to failure. Radiators demonstrated comparatively higher reliability, with an initial value of 18.3%, although this declined to 5.2% by 2024. Air hoses showed moderate performance, with reliability decreasing from 13.0% to 6.8% over the same period. Despite consistently high availability levels exceeding 90% across all components, the low reliability values highlight significant underlying performance deficiencies. The observed deterioration trends suggest the predominance of reactive, run-to-failure maintenance practices. In contrast, the G-3516B compressors demonstrated high uptime, reliability, and availability (>92%) under variable load conditions, highlighting the impact of operational control and preventive maintenance. The findings underscore the need for a transition to proactive maintenance strategies, including component-specific preventive maintenance schedules and systematic maintenance performance tracking, to enhance system reliability and operational efficiency. This study provides practical insights for maintenance optimization and supports data-driven decision-making in industrial compressor operations. Monte Carlo simulations effectively captured stochastic failure behavior, probabilistic uptime, and production performance, providing realistic projections under uncertainty. The findings underscore the importance of component-specific preventive and predictive maintenance strategies, reliability-centered design, and operational optimization to improve system efficiency, reduce unplanned downtime, and enhance lifecycle cost-effectiveness in industrial compressor operations.