FREMONT, CA: PrecisionHawk Inc., a leading provider of drone technology, launched its latest product, PrecisionAnalytics Energy, which is an aerial mapping, modeling, and inspection platform. It employs the principles of cutting-edge artificial intelligence (AI) and machine learning (ML) to analyze the acquired data and produce actionable information.
The customers of PrecisionAnalytics can slash their inspection costs, and at the same time, enhance field safety. PrecisionHawk is one of the few unmanned aerial vehicle (UAV) companies with the capability of operating beyond visual line of sight (BVLOS) drones. It boasts of more than 150 full-time drone operators and around 15,000 service providers in the drone operator network. PrecisionAnalytics users can leverage the expansive operator network to collect data over ten thousand miles of infrastructure.
Some of PrecisionHawk’s popular market solutions include:-
Many of the world’s leading wind turbine original equipment manufacturers (OEM) and service personnel using PrecisionAnalytics Wind have significantly reduced manual inspection and the costs associated with it. The platform delivers automated reports and can detect problems such as leading-edge corrosion, lightning strike damage, cracks, wearing, degradation of gel coat, and UV radiation damage using its AI change detection features. The platform reduces hazardous manual work and increases uptime through preventive maintenance.
Ground teams employing traditional methods of inspection find 10 percent of the distribution poles to be out of compliance. This data was proved inaccurate by PrecisionAnalytics team, which found the actual number closer to 25 percent. They used their cloud-based platform for distribution, which leverages AI and ML to identify the component damage. It analyzes the imagery with the distribution pole data, producing a collection of imagery as per the asset health.
The solutions offered by PrecisionAnalytics Transmission are designed to assist hazardous inspections of helicopters, reducing the associated costs by almost 28 percent. It provides comprehensive insight into the condition of the systems, applying machine vision to identify issues with critical components such as cracked insulators, structural damage, and corrosion. It consolidates the gathered data to draw infrastructure statistics vegetation maps, detailed views, and historical record keeping.
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