A Transformative Internship Experience at ZF Wind Power

During my internship at ZF Wind Power Coimbatore Private Limited, which took place from July 9th to August 8th, 2024, I had the chance to leverage my academic background in Artificial Intelligence and Data Science to tackle practical challenges in the quality control department. I collaborated with a dedicated team consisting of Krishneshwaran D ,T. Dilan Melvin , and Jaya Surya . Throughout this journey, our mentor, Mr. Arun Sundar S , provided invaluable guidance and support, enriching our experience significantly.

About ZF Wind Power

ZF Wind Power stands as a global leader in delivering advanced gearbox solutions for both onshore and offshore wind turbines. The company's strong commitment to innovation and excellence in manufacturing is evident through its global reach and state-of-the-art facilities, including the cutting-edge plant in Coimbatore, India.

Our Internship Projects

1. Concentration Drop Prediction for Parts:

Objective: Our first project aimed to identify and quantify the concentration drop associated with each part, a metric not directly measurable by sensors.

Methodology: We collected and cleaned four years of historical data, applying a Weighted Average Calculation algorithm to pinpoint the concentration drop caused by each part. This real-time calculation enabled more precise monitoring and adjustments to part usage, ensuring optimal concentration levels.

Outcome: We developed an interface to store the calculated values, which included critical data like part types, usage dates, and current concentration levels.

2. Time Series Prediction of Concentration Values:

Objective: The second project focused on predicting future concentration values to ensure they remained within specified thresholds.

Approach: We employed two predictive models—a Gradient Boosting Regressor and a Long Short-Term Memory (LSTM) model. Trained on historical data, the LSTM model proved particularly effective in capturing temporal dependencies.

Outcome: These models delivered accurate predictions, crucial for maintaining optimal concentration levels and preventing issues.

This internship marked a pivotal point in our education, providing us with the opportunity to apply theoretical knowledge to real-world challenges. Collaborating as a team and benefiting from the mentorship of Mr. Arun Sundar S , we honed our technical skills and laid a strong foundation for our future careers in engineering and data analysis. I would also like to express my heartfelt gratitude to Mr. Sreenivas Naidu FIE , whose recommendation opened the door to this remarkable opportunity.