Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Rapidly evolving renewable energy generation technologies and the ever-increasing scale
of renewable energy installations are driving the need for more accurate, faster, and smarter …

Due to often working in the environment of variable speeds and loads, it is an enormous
challenge to achieve high-accuracy fault diagnosis (FD) of rolling bearings (RB) via existing …

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its
strong feature representation capability in recent years. Nevertheless, in engineering …

Collaborative fault diagnosis has become a hot research topic in fault detection and
identification, greatly benefiting from emerging multisensory fusion techniques and newly …

Data-driven fault diagnosis techniques are frequently applied to ensure the reliability and
safety of industrial systems. However, as a common challenge, the class-imbalance problem …

Deep learning has gained significant success in fault diagnosis. However, the number of
gearbox health samples is inevitably much larger than that of fault samples in real-world …

Component reliability plays a pivotal role in industrial systems, which are evolving with
larger complexity and higher dimensionality of data. It is insufficient to ensure reliability and …

The gearbox is a critical component in the mechanical system, requiring vigilant monitoring
to prevent adverse consequences on safety and quality due to malfunction. Therefore, early …

Fault diagnosis is subject to the challenge of implementing model learning in the presence
of small samples and imbalanced data (ie, variable operating conditions), which is a …