Thread provides effective autonomous inspection options to utility, oil and gas, and renewable resource industries that require scheduled and on-demand asset assessments.

Thread’s flagship product, UNITI (Unified Inspection Technology Interface), enables rigorous and repeatable robotic and sensor data acquisition without the use of external contractors. Inspection quality increases with consistent and context aware imaging for timely defect identification. High quality, post inspection data that is asset-indexed enables defect alerting and long-term time-based analyses, such as measuring flaw or damage growth rates and implementing severity rating systems.

Such analyses trigger effective asset management with enterprise-ready reporting, actionable insights, and integrated information that will enable a faster return-to-service. In-house and on-demand inspection-based data workflows raise the level of inspection standards because control of what is inspected and when it is necessary is now in complete control of the asset owner. This results in the ability to make decisions with unprecedented statistical precision for timely prevention of asset degradation and loss.

Enterprise-Scale Asset Inspection is yet to be realized:

Utility and renewable resource industries struggle with proper asset inspections presently because of inconsistent monitoring and laborious methods of data interpretation and management. Timely and accurate asset condition assessments are essential for continuous functionality and minimized downtime. However, the significant cost and accuracy of inspection options available to customers currently result in meeting minimal regulatory or internal requirements, instead of an effective tool to enhance asset return on investment.

Methods that require in-person observations are inaccurate, time consuming, expensive, and are often dangerous. As a result, robotic data collection systems have become the norm in providing timely information to prevent losses, but available monitoring devices can have significant drawbacks. Ready-to-fly consumer drones were first launched in 2010 and were originally used sparingly in industry for various unplanned inspections, such as flare stack inspections. From these situations, the value of drone operations became apparent and consequently the use of drones increased, but in a limited manner due to functional inadequacies.

The operational platforms for drone use are constrained by the requirement for a high degree of operator skill. This requirement limits the practical use of drones to higher value inspection activities that can be scheduled in advance with an outside service provider. At present, many oil and gas companies working to implement an internal inspection program attempt to use consumer software to manage the skill barrier, but this solution generates a large amount of raw data that can be too burdensome and costly to review, which reduces the capture platform efficiency. In addition, the result may not provide the necessary consistency for objective comparisons over time. Software tools of today for automated robotic data collection were built to rely on waypoints for mapping and modeling missions. This method forces users to struggle with employing these limited and cumbersome tools in workflows that need to be scalable across regional, national and international enterprise operations. Implementation in this manner is simply not possible because the data generated is scattered and typically one-time-use (throw away) and lacks leverage across organizations and common approaches to asset modalities. As a result, enterprise level use of drones and other robotic systems to provide valuable inspection-based insights, both regularly and on an as-needed basis, is not a viable option.

Thread provides the essential element that is missing in the asset monitoring industry by using state-of-the-art image location tracking and edge computing to minimize or alleviate follow up inspections due to a lack of information or inconsistent data collection. Organization and contextualization of successive asset images in time and space for proper inspection and analysis requires a level of consistency that is unavailable using conventional methods. Use of edge computing for real time situational awareness and accurate positioning is integral to the Thread survey system and enables precise repositioning of your monitoring robotics between inspection sessions, that allows for immediate image comparisons in adaptive machine learning and vision.