This paper reviews the recent literature on machine learning (ML) models that have been
used for condition monitoring in wind turbines (eg blade fault detection or generator …

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating
machinery (RM) have critical importance for early diagnosis to prevent severe damage of …

The scope of data-driven fault diagnosis models is greatly extended through deep learning
(DL). However, the classical convolution and recurrent structure have their defects in …

We develop a novel deep learning framework to achieve highly accurate machine fault
diagnosis using transfer learning to enable and accelerate the training of deep neural …

Fault diagnosis of rotating machinery is a significant engineering problem. In recent years,
fault diagnosis methods have matured based on the Convolutional Neural Network (CNN) …

With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on
data-driven methods has become the most effective solution to address smart manufacturing …

Fault diagnosis is vital in manufacturing system, since early detections on the emerging
problem can save invaluable time and cost. With the development of smart manufacturing …

Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating
machines, balanced training data for different machine health conditions are assumed in …

Rotating equipment is considered as a key component in several industrial sectors. In fact,
the continuous operation of many industrial machines such as sub-sea pumps and gas …

In the past years, data-driven approaches such as deep learning have been widely applied
on machinery signal processing to develop intelligent fault diagnosis systems. In real-world …