The smart city vision has driven the rapid development and advancement of interconnected
technologies using the Internet of Things (IoT) and cyber-physical systems (CPS). In this …

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 …

Existing studies on meta-learning based few-shot fault diagnosis largely focus on constant
speed scenarios, neglecting the consideration of more realistic scenarios involving unstable …

Fault diagnosis of bearing under variable working conditions is widely required in practice,
and the combination of working conditions and fault fluctuations increases the complexity of …

Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of
modern industrial systems. For safety and cost considerations, critical equipment and …

While transfer learning-based intelligent diagnosis has achieved significant breakthroughs,
the performance of existing well-known methods still needs urgent improvement, given the …

Machinery signals typically consist of multiple sub-signals in different frequency bands,
while existing Transformer-based fault diagnosis methods often lack attention to key fault …

Identifying unseen classes with limited labeled data for reference is a challenging task,
which is also known as few-shot learning. Generally, a knowledge-rich model is more robust …

Artificial intelligence (AI) has advanced rapidly, but it has limited impact on biomedical text
understanding due to a lack of annotated datasets (aka few-shot learning). Multi-task …

Traditional deep learning fails to identify new faults when the number of faulty samples is
limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not …