A Novel Benchmark for Fault Detection in Rolling Bearings Using CNNs and Monte Carlo Dropout

Predictive maintenance is crucial in modern industrial settings, as it minimizes unexpected failures and optimizes operational costs. Rolling bearings, essential components in rotating machinery, are particularly prone to wear and faults, necessitating continuous real-time monitoring. This paper introduces a novel dataset comprising vibrational data collected from bearings under varying operational conditions on a test bench. A Convolutional Neural Network (CNN) is employed for fault classification, leveraging domain-specific feature selection, including raw acceleration signals and their envelope extractions. Additionally, Monte Carlo (MC) dropout is applied to quantify prediction uncertainty, enhancing trust in AI-driven fault detection. Results demonstrate that incorporating feature engineering and dataset balancing significantly improves classification accuracy, while MC dropout provides valuable uncertainty estimates. The proposed approach contributes to more reliable and interpretable machine learning models for industrial fault diagnosis