A. Hachem

Machine learning versus model-driven solution for unbalance and misalignment fault detection

Experimental results indicate that XGBoost achieves the highest accuracy in identifying unbalance, outperforming the neural network and the Bayesian model and for misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.