Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

As failure data is usually scarce in practice upon preventive maintenance strategy in
prognostics and health management (PHM) domain, transfer learning provides a …

Aiming at limitations in fully exploiting the temporal correlation features of the original
signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training …

The varying speed can cause the significant data distribution shift of bearings, making it
difficult for deep learning-based bearing fault diagnosis models to ensure good …

Through the application of deep learning and multi-sensor data, fault features can be
automatically extracted and valuable information can be integrated to tackle intricate …

Motor bearing fault diagnosis is essential to guarantee production efficiency and avoid
catastrophic accidents. Deep learning-based methods have been developed and widely …

In bearing fault diagnosis, the faulty data are generally limited due to the high cost of fault
signal collection. Considering the excessive parameters in the traditional convolutional …

As a critical component of a tunnel boring machine (TBM), the precise condition monitoring
and fault analysis of the main bearing is essential to guarantee the safety and efficiency of …

Timely and accurate data-driven fault diagnosis approaches are essential for ensuring the
reliable operation and efficient maintenance of rotating machinery. However, practical …

Recent advances in intelligent rotating machinery fault diagnosis have been enabled by the
availability of massive labeled training data. However, in practical industrial applications, it is …