Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Fault diagnosis and prognosis (FDP) tries to recognize and locate the faults from the
captured sensory data, and also predict their failures in advance, which can greatly help to …

The hydro-turbine is the core equipment of the hydropower station, and it is essential to
diagnose and identify its faults. A fault diagnosis model based on Bayesian optimization …

Convolutional neural networks (CNNs) have demonstrated promising effectiveness in
vibration-based fault diagnosis. However, the faulty characteristics are usually distributed on …

Owing to the harsh operating environment of rolling bearings, acquired vibration signals
contain strong noise interference, which makes it challenging for conventional methods to …

Vibration-based condition monitoring techniques are commonly used to detect and
diagnose failures of rolling bearings. Accuracy and delay in detecting and diagnosing …

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is
very important for its stable operation. The information fusion of multisensor data has been a …

Deep learning methods have been widely investigated in machinery fault diagnosis owing to
their powerful feature learning capability. However, high accuracy is hard to achieve due to …

Using deep learning in three-phase induction motor fault diagnosis has gained increasing
interest nowadays. This paper proposes a Convolutional Neural Network (CNN) model to …

Multisensor information are usually required to recognize the health condition of machinery
by domain experts, since redundancy and complementarity of multisensor information can …