The traditional deep learning-based bearing fault diagnosis approaches assume that the
training and test data follow the same distribution. This assumption, however, is not always …

Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to
machine fault diagnosis. This is a promising way to release the contribution from human …

预测与健康管理对保障机械装备安全服役, 提高生产效率, 增加经济效益至关重要.
高质量的全寿命周期数据是预测与健康管理领域的基础性资源, 这些数据承载着反映装备服役 …

Detection accuracy of bearing faults is crucial in saving economic loss for industrial
applications. Deep learning is capable of producing high accuracy for bearing fault …

In recent years, advances in computer technology and the emergence of big data have
enabled deep learning to achieve impressive successes in bearing condition monitoring …

Health condition monitoring of rotating machinery is a crucial task to guarantee reliability in
industrial processes. In particular, bearings are mechanical components used in most …

Bearing fault diagnosis is a significant part of rotating machine health monitoring. In the era
of big data, various data-driven methods, which are mainly based on deep learning (DL) …

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating
machinery (RM) has a vital role in the modern industrial world. However, the remaining …

In condition based maintenance, different signal processing techniques are used to sense
the faults through the vibration and acoustic emission signals, received from the machinery …

Condition-based monitoring and machine fault detection play important roles in industry as
they can ensure safety and reduce breakdown loss. Weak signal detection is an essential …