Convolutional Neural Network with Mix-Up Data Augmentation for Ball Bearing Fault Diagnosis

Ball bearings are vital for rotating machinery, requiring reliable fault diagnosis. Traditional approaches rely on manual feature extraction, ‎requiring specialised expertise. Deep learning reduces human input but struggles with capturing global input context, integrating statistical ‎features, and computational costs. This work introduces a CNN-based fault diagnosis method with Mix-up augmentation. Vibration signals ‎are transformed into 2D time-frequency images via Continuous Wavelet Transform (CWT) to retain temporal-spectral information. Mix-up ‎enhances dataset diversity, improving model robustness. CNNs then classify fault type and severity using these augmented inputs. Evaluat-‎ed on experimental and CWRU datasets, the approach surpasses state-of-the-art methods in accuracy and stability. Combining CWT’s de-‎tailed analysis, Mix-up’s data enrichment, and CNNs’ automated feature extraction resolves prior limitations, delivering an efficient solution ‎for industrial fault detection. The framework ensures reliable, resource-effective automation, advancing predictive maintenance in rotating ‎machinery‎.