关键词: 设备精益管理, 在线监测, 一体化管控, 预测性维护, 装置检维修

Abstract: In the new phase where informationization transformation converges with quality enhancement and efficiency improvement in oil and gas fields, equipment lifecycle management faces challenges of "numerous objects, extended chains, fragmented scenarios and diverse standards." To address this, the paper proposes a "data-driven lean equipment management" concept and constructs an integrated digital management system, advancing equipment management toward digital and intelligent transformation. This system employs a four-tier architecture: Perception—Processing—Intelligence—Governance. At the perception layer, cross-device multi-protocol online monitoring technology is developed to achieve real-time data aggregation and status perception. At the processing layer, an innovative equipment integrity integrated control technology is proposed, utilizing QR codes to realize full-process tracking of collection, repair and inspection, connecting the inspection-diagnosis-maintenance chain, and enhancing transparency and execution efficiency. At the intelligence layer, a KPI indicator system and health index model are formed, and support vector regression is introduced to carry out compressor energy consumption prediction and condition early warning, strengthening predictive maintenance. In addition, considering the characteristics of natural gas processing units, intelligent inspection and maintenance control is promoted to enhance the intrinsic safety level of operational processes. Practical applications demonstrate that this framework effectively resolves current challenges in oil and gas field equipment management. It significantly enhances the granularity and intelligence level of equipment management, reduces operational and maintenance costs, ensures safe and stable equipment operations and provides robust support for oil and gas enterprises in achieving quality-efficiency dual improvement and high-quality development.

Key words: equipment lean managementlonline monitoringlintegrated controllpredictive maintenanceldevice inspection and maintenance