Despite a few recent meta-learning studies have facilitated few-shot cross-domain fault
diagnosis of bearing, they are limited to homogenous signal analysis and have challenges …

Transformer has been widely applied in the research of rotating machinery fault diagnosis
due to its ability to explore the internal correlation of vibration signals. However, challenges …

The cross-machine diagnosis of CNC spindle motors with compound faults is essential and
challenging because of the subsystem coupling and individual difference. This paper …

Data-driven fault diagnosis approaches have attracted considerable attention in the past few
years, and promising diagnostic performance has been achieved with sufficient monitoring …

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

Deep forest models offer a promising alternative to traditional deep neural networks by
demanding fewer training samples and hyperparameters. However, existing deep forest …

Compound fault identification has always been a challenge in bearing fault diagnosis.
Existing learning-based compound fault diagnosis methods require numerous labeled …

In recent years, data-driven methods have been widely used in the field of high-voltage
circuit breakers (HVCBs) fault diagnosis. However, due to the complex mechanical structure …

Intelligent fault diagnosis employing CNN-based techniques has demonstrated promising
results in rotating machinery maintenance and management. However, most approaches …

Ensuring the safety and reliability of rotating machinery in modern industrial production and
intelligent manufacturing is of paramount importance. While deep learning-based fault …