Research Insights
PFI-RP: ElectricDot: Advanced Anomaly Detection and Diagnostics for Electrical Devices and Networks
The broader impact of this Partnerships for Innovation – Research Partnerships (PFI-RP) project is to enhance the reliability and security of electrical devices and networks within modern infrastructure including, but not limited to, buildings, manufacturing systems, and hospitals. This PFI-RP project introduces a smart sensor capable of detecting anomalies, pinpointing their locations, and diagnosing issues in electrical devices and networks. The algorithms and designs developed may also contribute to the broader field of anomaly detection and diagnosis beyond electrical signals. The project team will provide training to undergraduate and graduate students, in addition to middle school teachers. Collaborations with the Peach State Louis Stokes Alliances for Minority Participation (LSAMP) and the NSF Research Experiences for Undergraduates (REU) programs will be nurtured to support these efforts. Strategic partnerships with industry leaders offer vital insights and provide educational and leadership opportunities for graduate students and postdoctoral researchers. The project brings together a strong partnership between academia and prominent industry leaders, including General Electric (GE), United States Robins Air Force Base (RAFB), Siemens America (Siemens), and NEC Laboratories America (NEC) to explore the commercialization of an electrical sensing technology for scalable anomaly detection and diagnosis in electrical devices and networks. The impact spans from small-scale applications (e.g., homes, buildings, factories) to large-scale scenarios (e.g., distribution networks to transmission networks of main grids). This adaptability facilitates dynamic data processing, allowing the installation of varying numbers of smart sensors. Additionally, the technology offers customized programming and low-cost, flexible deployments, which can be easily installed by plugging into an electrical outlet in residential and commercial settings.
Funder: National Science Foundation
Amount: $1,000,000
PI: Jin Ye, College of Engineering