What drove the decision to develop an AI-based use case?
The decision to develop an AI-based use case was driven by the need for a sophisticated and efficient solution to address critical challenges in wind energy production, specifically in monitoring and maintaining wind turbine blades. Traditional methods often fell short in timely detection of potential damage, leading to significant operational disruptions and increased costs. AI offered a smarter approach, analyzing data from sensors to spot subtle signs of damage quickly. Its ability to process large amounts of data and find small patterns was crucial. This meant we could catch issues early, preventing bigger problems and saving on maintenance costs in the long run.

What were your expectations or requirements for the AI application?
Our expectations for the AI application were centered on achieving precise and early detection of various damages in wind turbine blades. We are aiming for a system capable of swift data analysis from sensors, accurately identifying signs of potential damage such as cracks, erosion, or other issues. The AI application needed to handle large datasets effectively, continually learning and improving its ability to detect diverse types of damage. Real-time monitoring was a key requirement. We sought an AI system that could provide immediate updates and alerts regarding any detected issues. This functionality was critical for enabling proactive maintenance planning, allowing for timely interventions to prevent minor problems from escalating into significant issues that could disrupt operations.

What frameworks/methods did you rely on for development?
Since we are predominantly using deep learning for anomaly detection and machine learning to assess audio file quality, our main frameworks/methods include PyTorch, TensorFlow, Keras, and Scikit Learn.

The use of AI solutions must also be accompanied by a change in competences. How have you dealt with this challenge in respect of your employees?
We've established global training programs with mentors worldwide. Collaborating closely with research universities and academic institutions, our team benefits from a diverse pool of knowledge. Their backgrounds include robotics, machine learning, and various expertise areas, fostering a rich skill set. This networked approach ensures access to cutting-edge insights, enhancing our team's proficiency in implementing AI solutions.

In what timeframe did they implement the AI solution in your company/your client‘s company?
We began our work on the AI solution in 2020, and the entire process, including product development and initiating pilot projects, has spanned approximately 2 years. As of now, we are in the final stage of development, engaged in a total of 9 pilot implementations.

A conclusion to your AI solution:
Our AI solution, named Windrover, is a tool designed for early detection of issues in wind turbine blades. It works by analyzing data from sensors installed on turbines. We've developed Windrover to accurately identify potential damage, enabling proactive maintenance planning. This product helps wind energy producers save on repair costs by detecting problems early

One final piece of advice/tip to other entrepreneurs looking to apply AI:
My advice to fellow entrepreneurs venturing into AI is simple, persevere and disregard negative comments. Embracing AI can be challenging, but staying resilient and focused on your goals is key. Don't let skepticism deter your progress, instead, use it to fuel your determination. Trust in your vision, stay adaptable, and keep learning.

https://werover.com

Picture: Werover