Closing Knowledge Gaps Between Operations and Maintenance by Integrating Predictive Analytics and Asset Management

摘要

Managing the Operations and Maintenance (O&M) functions within a power generation utility has traditionally been the purview of professionals within their respective domains. Operations experts have been supported by tools for measuring plant performance, scheduling and planning generation and fuel demand, and assessing equipment health (collectively referred to in this paper as Predictive Analytics). Maintenance specialists are in turn supported by systems for work and maintenance management (referred to in this paper as Asset Management). However, the governance process by which these O&M activities and tools interact has been typically performed in an ad hoc manner, with high reliance on personal experience and with limited interchange of knowledge between the operations and maintenance specializations. Additionally, minimal detailed information from these groups is used in making strategic business decisions for capital planning and market trading (fuel, electricity) at corporate levels. Meanwhile, the O&M business functions are becoming more important than ever in mitigating consequences emerging from increasing load demand variability, environmental regulations, and shifting cost/competitive landscapes.