Fault diagnosis for rolling elements in rotating machinery persistently receives high research
interest due to the said machinery's prevalence in a broad range of applications. State-of-the …

Recently, deep transfer learning based intelligent fault diagnosis has been widely
investigated, and the tasks that source and target domains share the same fault categories …

Currently, convolutional neural networks (CNNs) have shown great potential in the field of
rotating machinery fault diagnosis. To maximize accuracy, the network architecture of novel …

Fault diagnosis is the key procedure to ensure the stability and reliability of mechanical
equipment operation. Recent works show that deep learning-based methods outperform …

Rotating machinery plays a key role in mechanical equipment, and the fault diagnosis of
rotating machinery is a popular research topic. To overcome the dependency on expert …

Rotating machinery is ubiquitous in modern industrial systems. Ensuring optimal operating
conditions for rotating machinery is essential to satisfy stringent requirements on safety …

In the case of a compound fault diagnosis of rotating machinery, when two failures with
unequal severity occur in distinct parts of the system, the detection of a minor fault is a …

The traditional intelligent diagnosis methods of rotating machinery generally require feature
extraction of the raw signals in advance. However, it is a very time-consuming and laborious …

Recently, deep neural network (DNN) models work incredibly well, and edge computing has
achieved great success in real-world scenarios, such as fault diagnosis for large-scale …

Aiming to promptly process the massive fault data and automatically provide accurate
diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of …