Sulzer Schmid, a Swiss company pioneering UAV technology for wind rotor blade inspection announces the launch of its latest 3DX™ Damage Progression module designed to track damages and monitor how they develop over time. By comparing the evolution of blade damages from one inspection campaign to another, the new module enables blade experts to make informed decisions for maintenance and repairs.
The new 3DX™ Damage Progression module allows users to compare damages which have been recorded during previous inspections with new inspection data. Thanks to the user-friendly interface, the progression of a damage is displayed in a time series of recorded inspections, so that it can be easily evaluated. In this way, leading-edge erosion for example, can be closely monitored and its evolution tracked in a so-called “damage chain”.
The rich inspection data is used to accurately identify, localise, measure, and classify damages. This means that for each subsequent inspection it is always possible to find the exact location and history of any damage, review, and evaluate its evolution. Knowing how damages develop over time allows blade experts to determine which damages need to be repaired when. This makes the planning of repair campaigns significantly more efficient, saving downtime and optimising blade repair budgets. Being able to compare inspection data with historical data also facilitates the quality assessment of repairs
This additional module is available on the proprietary browser-based 3DX™ Blade Platform, which integrates all the inspection information in one place, providing an overview of the blade health of the entire fleet of wind turbines.
Tom Sulzer, Sulzer Schmid Co-founder and CEO, states, “We are constantly innovating to increase the degree of automation for rotor blade inspections and that’s what makes our new Damage Progression module possible in the first place. When inspections are carried out in a routine and automated fashion the data generated becomes a veritable treasure. With advanced analytics and machine learning the data can generate important insights and trend analysis, which can be used to optimise repair campaigns and maintenance strategy. As more and more data are collected, we learn how problems develop over time and lay the foundation for predictive maintenance. The benefits are huge, resulting in substantial cost savings and better overall performance.”