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

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

Optimal Transport for Domain Adaptation

Domain adaptation from one data space (or domain) to another is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data space become more robust when confronted to data depicting the same semantic concepts (the classes), but observed by another observation system with its own specificities. Among the many strategies proposed to adapt a domain to another, finding a common representation has shown excellent properties: by finding a common representation for both domains, a single classifier can be effective in both and use labelled samples from the source domain to predict the unlabelled samples of the target domain. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labelled samples in the source domain to remain close during transport. This way, we exploit at the same time the few labeled information in the source and the unlabelled distributions observed in both domains. Experiments in toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others