This technology is a machine learning algorithm that estimates the mean time between failures (MTBF) for components in electrical networks.
With rising energy costs, efficiently using and maintaining the current energy grid has become a priority. Because of the complexity and breadth of assets in many locations across the energy grid, the management and decisions on replacing equipment can be difficult. As the assets in the system deteriorate, the probability of interruptions to the power supply increases. And so currently, many assets are often replaced before their true lifecycle, increasing costs. Current enterprise asset management systems cannot identify the most probable lifecycle of each individual asset, thus failing to maximize efficiencies while maintaining low failure rates in systems.
This technology is a machine-learning algorithm can be used to estimate the MTBF of components in electrical networks. Through utilization of historical and current inputs, the training of the algorithm allows for reliable predictions of areas where improvements in MTBF can be made. These suggestions can be applied to all categories of capital assets, allowing optimization of the replacement and upgrade of components. This technology can enable cost-effective solutions to be applied to the highly complex process of managing assets across energy grids. Through better management of equipment replacement in electrical grids, this technology can provide higher operating efficiency and reduce costs associated with repairs and upgrades.
IR M09-094
Licensing Contact: Richard Nguyen