Abstract Machine learning (ML) pervades an increasing number of academic disciplines and
industries. Its impact is profound, and several fields have been fundamentally altered by it …

Given the advancements in modern technological capabilities, having an integrated health
management and diagnostic strategy becomes an important part of a system's operational …

With the increase of the scale and complexity of mechanical equipment, traditional intelligent
fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the …

Abstract Since 2006, deep learning (DL) has become a rapidly growing research direction,
redefining state-of-the-art performances in a wide range of areas such as object recognition …

Given the growing amount of industrial data in the 4th industrial revolution, deep learning
solutions have become popular for predictive maintenance (PdM) tasks, which involve …

Fault diagnosis of rotating machinery plays a significant role in the industrial production and
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …

In the last few years, many works have addressed Predictive Maintenance (PdM) by the use
of Machine Learning (ML) and Deep Learning (DL) solutions, especially the latter. The …

The downtime of industrial machines, engines, or heavy equipment can lead to a direct loss
of revenue. Accurate prediction of such failures using sensor data can prevent or reduce the …

Fault diagnosis of rotating machinery is vital to improve the security and reliability as well as
avoid serious accidents. For instance, robust fault features are crucial to achieve a high …

Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them
are based on a basic assumption that training and testing data are adequate and follow the …