Benefiting from the excellent capability of data processing, deep learning-based methods
have been well applied in fault diagnosis. However, these methods may perform poorly due …

In recent decades, fault diagnosis methods based on machine learning and deep learning
have achieved excellent results in fault diagnosis and are characterized by powerful …

In the era of big data, various data-driven fault diagnosis algorithms, which are mainly based
on traditional machine learning and deep learning, have been developed and successfully …

Deep learning-based methods have been developed and widely used for fault diagnosis,
which rely on the sufficient data. However, fault data are extremely limited in some real-case …

In this article, we study the challenging few-shot fault diagnosis (FSFD) problem where
limited faulty samples are available. Metric-based meta-learning methods have been a …

Mechanical fault diagnosis is crucial to ensure the safe operations of equipment in intelligent
manufacturing systems. Deep learning-based methods have been recently developed for …

The techniques of machine learning, as well as deep learning (DL) methods, have seen a
wide application in the intelligent fault diagnosis field these years. However, contemporary …

Fault diagnosis is a key component of predictive system maintenance. Big data collected
from sensors helps create data-driven fault diagnosis methods. However, it may be …

Achieving deep learning-based bearing fault diagnosis heavily relies on large labeled
training samples. However, in real industry applications, labeled data are scarce or even …

Effective fault diagnosis on rotating machinery is crucial for ensuring the reliability and safety
of mechanical equipment. However, available fault data are frequently scarce in real …