Research and application of risk and condition based maintenance task optimization technology in an oil transfer station

Abstract

Oil transfer stations of PetroChina mostly scatter in Gobi, mountain areas or other sparsely populated areas, inconvenient transportation and absent professional engineers often delay the best time to repair the machines. Time-or interval-based maintenance (TBM) accounts for almost 100%, while, On-condition maintenance and other proactive maintenance are seldom adopted. TBM not only can't prevent happens of equipment fault but also cause the waste of the maintenance resource. In order to allocate maintenance resources reasonably, ascertain the minimum preventive maintenance requirement, ensure the reliability, availability and safety, this paper carries out a research on Risk and Condition Based Maintenance (RCBM) task optimization technology. Utilizing the internet of things (IOT), real-time database, signal-processing, Gray Neural Network, probability statistical analysis and service oriented architecture (SOA) technology, a Risk and Condition Based Indicator Decision-making System (RCBIDS) is built. RCBIDS integrates RCM, condition monitoring system (CMS), key performance management module, file management module, fault and defect management module, maintenance management module together, which aims to realize remote condition monitoring, maintenance technical support services (TSS), quantitative maintenance decision-making, and to ensure the Reliability, Availability, Maintainability and Safety (RAMS). The Predictive Maintenance Indicator model, reliability prediction model and Key Performance Indicator (KPI) model, which are embedded in the RCBIDS, are constructed separately. An engineering case shows that the risk and condition based maintenance task optimization technology can be used to optimize maintenance content and maintenance period, to minimize maintenance deficiencies and maintenance surplus, and to prolong the lifespan of equipment.