Abstract Multi-Source Information Fusion (MSIF), as a comprehensive interdisciplinary field
based on modern information technology, has gained significant research value and …

The accurate detection and diagnosis of faults in Liquid Rocket Engines (LREs) are critical
for ensuring space mission safety. However, the limited availability of actual fault samples …

Traditional deep learning methods rely on big data heavily, which makes bearing fault
diagnosis with small samples under variable working conditions a tricky problem. The …

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 …

Although Transformer has achieved excellent results in various tasks in industrial scenes,
owing to the environmental noise and cost limitation, the fault diagnosis approaches based …

Although data-driven fault diagnosis methods have achieved remarkable results, these
achievements often rely on high-quality datasets without noisy labels, which can mislead the …

Recent intelligent diagnostic algorithms for industrial practice have achieved impressive
results. However, due to safety considerations, complex environments and deployment cost …

The health condition of hydraulic axial piston pumps is crucial for the safety and reliability of
hydraulic transmission systems. Diagnosis results of traditional methods indicate the high …

Few-shot fault diagnosis aims to detect novel faults with only a few labeled samples in each
category. Most of the few-shot learning (FSL)–based fault diagnosis models use meta …

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial
for ensuring normal machinery operation. However, the nonlinear and non-stationary …