Automatizacija praćenja stanja reduktora vetroturbina - uporedna studija tehnike mašinskog učenja zasnovane na analizi vibracija

Abstract

Wind turbines play a role in the adoption of renewable energy production, but they are susceptible to shutdowns that require thorough monitoring. Gearbox failures are an issue leading to maintenance and operational downtime. This study investigates the application of machine learning methods to enhance the diagnosis of gearbox problems using vibration analysis. Through the application of fault scenarios that impact bearings and gears, the researchers successfully extracted time domain features from vibration data of a 750 kW turbine testbed in order to detect indications of damage. Support Vector Machine (SVM), Naive Bayes, and K Nearest Neighbour (KNN) machine learning models were used to classify gearbox faults. Among these models, Naive Bayes achieved an accuracy rate of 95.7%, which exceeded the established benchmarks. The probabilistic approach was able to successfully associate symptom characteristics with fault patterns. Intelligent monitoring systems could improve maintenance efficiency. This data-driven approach highlights the potential of machine learning in supporting wind power development by eliminating gearbox inefficiencies and improving turbine reliability, and further research is being conducted to ensure that this approach works in concert with diversity and in the real world. This shows how machine learning is contributing to advances in renewable energy by helping to analyze predictive problems and prevent costly gearbox failures.