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OFIL develops cloud-based Gridnostic software for predictive analytics

OFIL Systems has announced Gridnostic, the industry’s first dedicated high-voltage audit data analytics software. Gridnostic presents findings, such as risk visibility and prioritization insights, through a holistic view based on raw field data that is detailed within a Geographic Information System (GIS) platform. OFIL says it represents a breakthrough in predictive analytics, which is critical to ensuring the reliability of high-voltage networks that underpin telecommunications operations.

Current inspection techniques that rely on visual imaging methods often do not provide quantitative data and are prone to subjective interpretation. OFIL addresses this challenge by integrating decades of experience in Gridnostic and partnering with Scopito to leverage their data management capabilities. This collaboration enables automated analysis and contextualization of findings, transforming field data into a comprehensive visual map and severity score without changing current data collection processes.

5G Industrial Metaverse
5G Industrial Metaverse

As Gridnostic is increasingly adopted by more organizations, OFIL plans to continually incorporate evolving data into its analytical capabilities, providing users with the most up-to-date information on the maintenance of high-voltage assets and enabling long-term maintenance planning.

Giora Levi, CEO of OFIL

Historically, decision-making around HV and MV power line inspections has relied on large amounts of field data that has been subjectively analyzed and poorly managed, making long-term trend analysis difficult. Intelligently analyzing findings and prioritizing actions means stakeholders have what they need to avoid outages and disruptions.

Christian Christiansen, Special emergency preparation and maintenance DK2, Energinet

We faced challenges in analyzing and storing UV video data from our drone inspections, which is critical to maintaining our transmission towers. Integrating Gridnostic OFIL allowed us to seamlessly manage and analyze this data alongside existing inspection data, ensuring precise and efficient maintenance operations.