Predictive Asset Management Under Weather Impacts Using Big Data, Spatiotemporal Data Analytics and Risk Based Decision-Making

This paper introduces predictive framework for asset management risk assessment that utilizes big data. It describes the data analytics required for integrating the big data in time and space. The proposed assets management framework is novel since it implements a dynamic proactive maintenance scheduling. The dynamic maintenance scheduling overpowers the conventional periodic scheduling, by taking into account variety of factors that are changing over time and generating up to date estimations of components’ state of risk. The framework complements the condition-based maintenance scheduling by providing additional knowledge extracted from historical data and measurements taken from the sources besides the utility measurements, such as weather data. This enables the integration of various types of ever-changing environmental impacts into the assets failure assessment leading to prevention through optimized maintenance. The framework is demonstrated with the example of the predictive line insulator maintenance scheduling. Keywords—asset management; big data; reliability, resilience, risk analysis; weather forecast;