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 …

Due to the concurrency and coupling of various types of faults, and the number of possible
fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in …

Existing deep learning methods commonly requires massive labeled data for compound
fault diagnosis, which is difficult and time-consuming to collect in the real application. This …

Most data-based bearing fault intelligent diagnosis methods have assumed that all data is
under the same working conditions. However, the fault data under unknown working …

Bearing faults of rotating machinery are common compound faults, and diverse fault
categories are coupled, which makes it challenging to achieve state monitoring. For this …

Traditional data-driven intelligent fault diagnosis methods have been successfully
developed under the closed set assumption (CSA). CSA-based fault diagnosis assumes that …

Recently, deep-learning-based fault diagnosis methods have been widely studied for rolling
bearings. However, these neural networks are lack of interpretability for fault diagnosis …

Recently, with the urgent demand for data-driven approaches in practical industrial
scenarios, the deep learning diagnosis model in noise environments has attracted …

In recent years, deep learning has been extensively applied to bearing fault diagnosis with
remarkable achievements. However, in real industrial scenarios, the primary challenge in …

Conventional intelligent bearings fault diagnosis methods generally extract fault features
with a single channel, which seriously limit the features richness and the diagnostic …