Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults
  Detection Using Multiple Deep Learning Training Strategies and A Novel Double
  Loss Function
Khoa Tran Lam Pham Hai-Canh Vu
This paper presents a comprehensive analysis of motor bearing fault detection
(MBFD), which involves the task of identifying faults in a motor bearing based
on its vibration. To this end, we first propose and evaluate various machine
learning based systems for the MBFD task. Furthermore, we propose three deep
learning based systems for the MBFD task, each of which explores one of the
following training strategies: supervised learning, semi-supervised learning,
and unsupervised learning. The proposed machine learning based systems and deep
learning based systems are evaluated, compared, and then they are used to
identify the best model for the MBFD task. We conducted extensive experiments
on various benchmark datasets of motor bearing faults, including those from the
American Society for Mechanical Failure Prevention Technology (MFPT), Case
Western Reserve University Bearing Center (CWRU), and the Condition Monitoring
of Bearing Damage in Electromechanical Drive Systems from Paderborn University
(PU). The experimental results on different datasets highlight two main
contributions of this study. First, we prove that deep learning based systems
are more effective than machine learning based systems for the MBFD task.
Second, we achieve a robust and general deep learning based system with a novel
loss function for the MBFD task on several benchmark datasets, demonstrating
its potential for real-life MBFD applications.