Automated Predictive Monitoring and Diagnosis in the Energy and Natural Resources Sector

Abstract

This paper explores strategies for automated, predictive monitoring and diagnosis (M&D) using advanced intelligent systems technologies within the Energy and Natural Resources (ENR) sector to address safety implications and costs associated with downtime and emergency breakdown repairs.  Automated, predictive M&D lends itself towards application within centralized M&D centers to allow monitoring across an entire fleet, to improve efficiency, reduce duplication of functions and allow for consistent, best practice operation and higher quality repairs across distributed assets.  However, centralization is often accompanied by a reduction in the number of on-site personnel and loss of critical knowledge for the operation and maintenance of assets.  Therefore, the selection of appropriate data, sensors, algorithms, a suitable platform, analytical tools, visualizations and ERP/CMMS integration form the basis for automated and predictive M&D of asset performance (intelligent Asset Performance Management (iAPM)) making it possible to detect and diagnose issues across geographically dispersed assets so that results are available in daily operational workflow for executive, analytical and operational personnel before they impact production, operations and safety.