Track: Undergraduate Research Competition

Abstract

Prices' fluctuations and challenging environments in the Oil industry pushed companies to improve their productivity. They made constant pressures to improve Key Performance Indicators by improving efficiency and reliability and reducing operating costs. This work aims to present a Smart predictive maintenance framework in the Oil industry, which provides better process availability by reducing downtime and maintenance costs. This conceptual Smart Predictive maintenance framework arranges for a safer process by providing continuous process and device diagnostics, increases the plant's availability; reduces verification effort by providing real-time documented verification without process interruption; and enables near-zero downtime in operations by providing information for real-time predictive maintenance. Refinery personnel will be provided with real-time information about equipment failure probability and predicted services, and maintenance time. Moreover, predictive maintenance actions for specific parts are suggested accordingly to allow near-zero downtime in operations. A technical and economic feasibility studies were performed on applyingthe proposed conceptual smart predictive maintenance framework within an Oil company in Kuwait. This workhelped practitioners, such as asset maintenance engineers and managers, to develop smart predictive maintenance infrastructure and its implementation in their organizations.