Recently, a considerable literature has grown up around the few-sample fault diagnosis
task, in which few samples of fault data are available for model training. The lack of fault …

The scarcity of fault samples has been the bottleneck for the large-scale application of
mechanical fault diagnosis (FD) methods in the industrial Internet of Things (IIoT). Traditional …

With the coming of the industrial Big Data era, data-driven fault diagnosis models emerge
recently and show potential results in many studies. However, it is impractical to collect …

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all
fault classes. There are massive target faults that cannot be collected in advance, which may …

Data-driven intelligent diagnosis models expect to mine the health information of machines
from massive monitoring data. However, the size of faulty monitoring data collected in …

Recently, deep learning-based intelligent fault diagnosis methods have been developed
rapidly, which rely on massive data to train the diagnosis model. However, it is usually …

Data-driven fault diagnosis has been a hot topic in recent years with the development of
machine learning techniques. However, the prerequisite that the training data and the test …

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

Despite rapid advances in machine learning based fault diagnosis, their identical
distribution assumption of the training (source domain) and testing data (target domain) is …

Compound fault diagnosis of bearings has always been a challenge, due to the occurrence
of various faults with randomness and complexity. Existing deep learning-based methods …