Intelligent fault diagnosis based on data-driven models has shown excellent effectiveness in
recent years, significantly promoting diagnosis technology. However, data-driven models …

Data-driven fault diagnosis models often exhibit limited generalization abilities when trained
on small sample sizes, as commonly encountered in complex working environments such as …

Deep learning-based bearing fault diagnosis methods have been developed to learn fault
knowledge from massive data. Owing to the deficiency of fault samples and the variability of …

Deep Learning (DL) can diagnose faults and assess machine health from raw condition
monitoring data without manually designed statistical features. However, practical …

Data-driven methods have high requirements for data samples and the ideal state is to have
sufficient samples and labels for model training. However, due to the limited sample of …

Vibration-based fault diagnosis methods of ball screw are susceptible to noise and
transmission path. Moreover, the accuracy of supervised deep learning models depends on …

Intelligent fault diagnosis, detection, and prognostics (DDP) for complex equipment
prognostics and health management (PHM) have achieved remarkable breakthroughs …

The turnout switch machine (TSM) is the critical signal equipment of the interlocking system,
which directly affects the efficiency and safety of rail transit. However, the incomplete feature …

Fault diagnosis of rotating machinery based on deep learning requires many identical
labeled data to achieve satisfactory results. However, in engineering scenarios, few typical …

Data-driven predictive maintenance reduces manufacturing downtime, and complex process-
sensing relationships encourage the use of deep learning to automatically extract features …