Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

Although the existing generative adversarial networks (GAN) have the potential for data
augmentation and intelligent fault diagnosis of planetary gearbox, it remains difficult to deal …

The complex and changeable working environment of wind turbine often challenges the
condition monitoring and fault detection. In this paper, a new method is proposed for fault …

Intelligent data-driven machinery fault diagnosis methods have been successfully and
popularly developed in the past years. While promising diagnostic performance has been …

Rotating machinery intelligent diagnosis based on deep learning (DL) has gone through
tremendous progress, which can help reduce costly breakdowns. However, different …

In the past years, the practical cross-domain machinery fault diagnosis problems have been
attracting growing attention, where the training and testing data are collected from different …

Rolling bearing fault diagnosis is of great significance to the stable operation of rotating
machinery systems. However, the fault data collected in practical engineering is seriously …

In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …

Data-driven machinery fault diagnosis methods have been successfully developed in the
past decades. However, the cross-domain diagnostic problems have not been well …