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This paper presents a comprehensive analysis of motor bearing fault detection(MBFD), which involves the task of identifying faults in a motor bearing basedon its vibration. To this end, we first propose and evaluate various machinelearning based systems for the MBFD task. Furthermore, we propose three deeplearning based systems for the MBFD task, each of which explores one of thefollowing training strategies: supervised learning, semi-supervised learning,and unsupervised learning. The proposed machine learning based systems and deeplearning based systems are evaluated, compared, and then they are used toidentify the best model for the MBFD task. We conducted extensive experimentson various benchmark datasets of motor bearing faults, including those from theAmerican Society for Mechanical Failure Prevention Technology (MFPT), CaseWestern Reserve University Bearing Center (CWRU), and the Condition Monitoringof Bearing Damage in Electromechanical Drive Systems from Paderborn University(PU). The experimental results on different datasets highlight two maincontributions of this study. First, we prove that deep learning based systemsare more effective than machine learning based systems for the MBFD task.Second, we achieve a robust and general deep learning based system with a novelloss function for the MBFD task on several benchmark datasets, demonstratingits potential for real-life MBFD applications.

Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults  Detection Using Multiple Deep Learning Training Strategies and A Novel Double  Loss Function