A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function

Abstract

Motor bearing fault detection (MBFD) is critical for maintaining the reliability and operational efficiency of industrial machinery. Early detection of bearing faults can prevent system failures, reduce operational downtime, and lower maintenance costs. In this paper, we propose a robust deep learning-based system for MBFD that incorporates multiple training strategies, including supervised, semi-supervised, and unsupervised learning. To enhance the detection performance, we introduce a novel double loss function. Our approach is evaluated using benchmark datasets from the American Society for Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Center (CWRU), and Paderborn University's Condition Monitoring of Bearing Damage in Electromechanical Drive Systems (PU). Results demonstrate that deep learning models outperform traditional machine learning techniques, with our novel system achieving superior accuracy across all datasets. These findings highlight the potential of our approach for practical MBFD applications.