OREANDA-NEWS. December 01, 2015. “Ageing assets and workforce, an influx of networked micro-grids, and the proliferation of intelligent devices that form the smart-grid on the traditional power grid are challenging utilities to identify more effective and efficient processes to manage and monitor their critical assets—and to do so with high safety, reliability and compliance,” writes Badrinath Setlur. “Traditional and smart asset management have a common objective—aid in the reduction, minimization and optimization of asset lifecycle costs across all phases, from asset investment planning, all the way to operation and maintenance.” Excerpts:

“Optimizing the costs associated with each phase will remain among the key objectives of an asset-intensive utility organization. At present, preventive maintenance schedules prescribed by manufacturers are not enough to help utilities avoid asset failures. In order to improve customer satisfaction, utility organizations need to work towards avoiding unexpected outages, managing asset risks and maintaining assets before failure strikes.

According to a survey, reducing outages and shortening restoration times are the most significant challenges in the area of power distribution, with 58 percent of respondents recognizing the need for a mechanism to predict equipment failure.

More than ever before, utilities are looking towards predictive analytics to extend the life of assets, as well as to bring greater predictability to performance and health. By applying predictive analytics to smart asset management, utilities can realize asset lifecycle cost reduction while improving the accuracy of their decision-making, allowing them to plan and prioritize maintenance activities.

By working proactively to collect and distill historic and current information to create predictive models for future events, utilities can enhance customer satisfaction, reduce total cost of ownership, optimize the field force as well as improve compliance. In order to achieve these objectives, key solution components of the predictive asset analytics platform are an operations dashboard, an asset model, rules setup, and prediction notification.

As organizations venture forward on their predictive analytics journeys, the need to ensure that a predictive asset analytics solution fits into the overall strategy and future business requirements is vital. Additionally, most utilities might not have the right processes and data needed to support analytics solutions. Therefore, it is imperative to improve business processes and upgrade IT infrastructure to support any analytics solution before it is deployed.

The time has come for organizations to adopt a data-driven culture.”