It is necessary to monitor and evaluate health state of rotating machinery, which directly
affects the quality and productivity of manufacturing processes. At present, most of the …

Mechanical rotating components such as bearing and gear are widely used in various
industrial occasions. Fault diagnosis of rotating component can guarantee operational …

Health services for rotating machinery are essential to ensure safe industrial production. In
recent years, deep learning (DL) methods based on vibration analysis have been …

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring
to prevent adverse consequences on safety and quality due to malfunction. Therefore, early …

Rotating machinery plays a critical role in many significant fields. However, the
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …

To realize high‐precision and high‐efficiency machine fault diagnosis, a novel deep
learning framework that combines transfer learning and transposed convolution is proposed …

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with
complicated damage detection problems of mechanical systems. Nevertheless, this …

Deep learning-based fault diagnosis has been acclaimed for its superiority in adaptively
mining salient features. The monitoring data used as the input of deep learning typically …

Accurate and efficient rotating machinery fault diagnosis is crucial for industries to guarantee
the productivity and reduce the maintenance cost. This paper systematically proposes a new …

Rotary encoder signal, as the built-in position information, possesses a wide variety of
advantages over vibration signal and has aroused great interest in the field of health …