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

The diagnosis of faults in rotating machinery is essential for maintaining reliability and operational efficiency. Shaft misalignment and bearing unbalance represent two of the most prevalent defects in such systems. This study investigates the effectiveness of a model-driven approach compared with machine learning (ML) techniques for the automatic detection of these faults, using vibration features extracted from vibration signals as diagnostic inputs. The model-driven method is based on a Bayesian framework (Acoem Accurex), while the ML approaches include a fully connected neural network and extreme gradient boosting (XGBoost). Experimental results indicate that XGBoost achieves the highest accuracy (70%) in identifying unbalance, outperforming the neural network (66%) and the Bayesian model (58%). 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.