Anonymization techniques are widely used to make personal data broadly available for
analytics/data-mining purposes while preserving the privacy of the personal information …

In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

Mechanical fault diagnosis is crucial for ensuring the normal operation of mechanical
equipment. With the rapid development of deep learning technology, the methods based on …

The performance of intelligent fault diagnosis models is often hindered by the lack of
available samples, a common issue in both the few-shot learning and imbalanced learning …

Convolutional neural network (CNN)-based intelligent fault diagnosis approaches have
showcased remarkable performance in the assessment of machine safety. The data …

Few-shot fault diagnosis is a challenging problem for complex engineering systems due to
the shortage of enough annotated failure samples. This problem is increased by varying …

The performance of intelligent fault diagnosis models heavily depends on the amount of
monitoring data available. In the situations of monitoring data insufficient for fault diagnosis …

Under real-world conditions, faulty samples of key components (eg, bearings and cutting
tools, etc.) are typically limited and sparse. Additionally, their historical data is characterized …

In solving the data imbalance problem, most of the existing studies ignored the effect of the
number of samples on the diagnostic performance of intelligent fault diagnostic models …

In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML)
algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML …