Experimental investigation of shaft misalignment effects on bearing reliability through vibration signal analysis using machine learning and deep learning

Bearing failures account for approximately 40–70% of malfunctions in rotating machinery, with shaft misalignment recognized as a critical yet underexplored root cause of these failures. Despite its practical importance, the direct impact of parallel shaft misalignment on bearing fault prediction remains unaddressed, mainly in the existing literature. To fill this gap, the present study proposes a novel experimental framework utilizing a custom-designed Bearing–Shaft Misalignment Simulator, specifically engineered to induce and control varying degrees of parallel misalignment under realistic operating conditions. This setup enables systematic analysis of bearing vibration behaviour, representing a significant methodological advancement over prior studies that primarily rely on synthetic or public datasets. Six classification models—five machine learning algorithms (Multilayer Perceptron, Random Forest, Decision Tree, K-Nearest Neighbors, and Adaptive Boosting) and one deep learning model (Long Short-Term Memory, LSTM)—were evaluated for classifying four levels of misalignment severity. The results reveal a strong positive correlation between the magnitude of misalignment and vibration intensity, highlighting the escalation of dynamic instability in the bearing system. Statistical time-domain feature extraction notably improved the performance of classical models, with KNN achieving a maximum accuracy of 92.9%. In contrast, the LSTM model, trained directly on raw time-series data, outperformed all other models, achieving a classification accuracy of 99.7%.This study contributes a novel dataset, an original misalignment simulation platform, and a comprehensive comparative analysis of modelling approaches. The findings highlight the crucial role of parallel shaft misalignment in bearing degradation and demonstrate the superior capability of deep learning for early fault detection, representing a significant advancement in condition-based maintenance for industrial applications.