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

  • Authors

    • Dillip Kumar Baral Research Scholar, Department of Architecture, Veer Surendra Sai University of Technology, Burla, Odisha, India
    • Krishna Chandra Patra PhD research scholar, BPUT University, Rourkela, Odisha, India
    • Ambuja Behera GITA Autonomous College, BPUT, Odisha
    • Nameet Kumar Sethy Research Scholar, Indra Gandhi Institute of Technology, Sarang, BPUT
    • Dhiren Kumar Behera Mechanical Engineering, IGIT, Sarang, Odisha, India
    • Rabinarayan Sethi Mechanical Engineering, IGIT, Sarang, Odisha, India
    https://doi.org/10.14419/08hkh236

    Received date: July 2, 2025

    Accepted date: August 13, 2025

    Published date: January 12, 2026

  • Convolutional Neural Networks; Continuous Wavelet Transform; Fault Diagnostics; Data Augmentation; Scalogram

  • Abstract

    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.

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  • How to Cite

    Baral , D. K. ., Patra , K. C. ., Behera , A. ., Sethy , N. K. ., Behera , D. K. ., & Sethi, R. . (2026). Convolutional Neural Network with Mix-Up Data Augmentation for Ball Bearing Fault Diagnosis. International Journal of Basic and Applied Sciences, 15(1), 68-78. https://doi.org/10.14419/08hkh236