Systems Technology Solutions

Critical Infrastructure Inspection


What We Do

STS focuses on data analytics through machine learning and artificial intelligence. Understanding and solving complex problems gathered from raw data can be hard to comprehend. STS focuses on data analytics through machine learning and artificial intelligence. Understanding and solving complex problems gathered from raw data can be hard to comprehend. STS bridges the gap between data collection and actionable results by transforming data into meaningful and productive actions for growth.

STS has spent the previous eighteen months developing, prototyping, and validating multi-modal, sensor-driven analytics for unmanned system applications. STS is poised to take the next key steps in its maturation of the technology and production of systems for the precision agriculture and critical infrastructure inspection markets.

Our Solution

  • Fully autonomous vehicle operations
  • Multi-modal sensor data collection
    • LiDAR
    • Thermal
    • Acoustic
    • Visual
  • Artificial Intelligence-driven data analysis
  • Predictive analytics for condition-based maintenance and reliability improvements

The Autonomous Health Diagnostic and Prognostic System for Precision Agriculture

GreenAI (pronounced as "green-eye") is a multi-modal sensor suite developed by STS in collaboration with Sandia National Labs. GreenAI is an artificially intelligent sensor module which assists the farmer and crop advisers in keeping a crop healthy while increasing yield. GreenAI follows a simple idea of how to manage data: Sense, Understand, Decide, and Act.

  • Sense

  • Understand

  • Decide

  • Act

SkyAI is a multi-modal mountable sensor and computing module that can be mounted to a UAV platform or be used independently.  It combines a variety of various sensors to gain insight into wind turbine blades to detect defects that may have derived from the manufacturing process and erosion that comes with usage. SkyAI takes advantage of advance neural network tools and is being developed in conjunction with expert engineers from Sandia National Labs.

The Problem

As Wind Energy systems become more prevalent and the original turbines that were built become older, maintenance and inspection need to be easier and more efficient. Currently, it takes considerable time and energy to properly inspect the tower, blades, and turbine for damage. A person must climb to the top of the tower, then hang from the turbine to visually inspect the blades and take handwritten notes of their observations. Not only is this dangerous but also extremely time-consuming. The inspection results must then be analyzed and a maintenance plan created. Some companies have attempted to reduce the time and cost of inspection by using land-based cameras with high resolution and high powered zoom lenses to take pictures of the blades, however, if something is found a person is still required to make a visual inspection in person.

The Opportunity

The US Department of Energy's Wind Vision Roadmap estimated that wind power will supply 35% of national electricity demand by 2050. In order to reach this goal, wind energy requires a reduction in unplanned maintenance by improving condition monitoring systems and the reliability of wind turbine performance.

Unmanned Aerial Systems (UAS) are emerging as a key solution. Current solutions to wind turbine malfunctions are manual (such as having a person scale the turbine), current UASs are piloted and only collect visual data, and images need to be culled and analyzed manually. STS is in partnership with Emerging Technology Ventures, Sandia National Laboratories, and the New Mexico Small Business Assistance program to create a fully autonomous UAS for wind turbine inspection.

We Look Forward to Hearing from You

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