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 …

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 …

Deep learning (DL) method are extensively used for bearing fault diagnosis (BFD). Due to
severe data distribution difference under variable working conditions, they have …

Data-based fault diagnosis is an important technology in modern manufacturing systems.
However, most of these diagnosis methods assume that all the data should be identically …

Bearing fault diagnosis plays an important role in rotating machinery equipment's safe and
stable operation. However, the fault sample collected from the equipment is seriously …

In this article, a challenging fault diagnosis task is studied, in which no samples of the target
faults are available for the model training. This scenario has hardly been studied in industrial …

Class imbalance issue has been a major problem in mechanical fault detection, which exists
when the number of instances presenting in a class is significantly fewer than that in another …

Data-based intelligent fault diagnosis method is a research hotspot in modern mechanical
systems. However, due to practical limitations, fault samples under all working conditions …

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 …

It has always been an issue of significance to diagnose compound faults of machines.
Existing intelligent diagnosis methods have to be trained by sufficient data of each …