… CNN and RNN, we propose a fault diagnosis model named WSAFormer-DFFN, a hybrid …
a rotating machinery fault diagnosis model named WSAFormer-DFFN. Powerful fault feature …

… In this study, the data collected by the machinery fault simulator (MFS) platform were chosen
to further demonstrate the effectiveness of the proposed approach in practical applications. …

… Acoustic-based diagnosis (ABD) is a promising method for rotating machinery fault
detection in real-industrial fields due to its advantage of non-contact measurement by air-couple. …

… of large-scale machinery. To achieve quick and precise fault diagnosis, this study proposes
… Unit (BiGRU), and a multi-head self-attention mechanism. The key advantage lies in its …

… mechanism is not used to guide the feature learning for the multi-sensor fault diagnosis. To
… multi-head graph attention network (MMHGAT) for large rotating machinery fault diagnosis. …

… applied to intelligent fault diagnosis of rotating machines. In … multi-head self-attention
convolutional neural network (TSMSCNN) for bearing fault diagnosis to solve the above problems. …

… multi-head self-attention convolution neural network (MSA-CNN) for bearing fault diagnosis.
… His research interests include machinery condition monitoring, intelligent fault diagnostics, …

… of machinery … problem, a new supervised multi-head self-attention autoencoder (SMSAE)
is proposed for extracting the HI that effectively reflects the degraded state of rotating machinery…

… The multi-head attention was introduced to GATs inspired by filters in CNN, which help the
… in the field of rotating machinery fault diagnosis. Therefore, a new fault detection method for …

… and extract information by Self Attention only, without relying on … field of intelligent fault
diagnosis (IFD) of machinery. The AM … the multi-head mechanism into Temporal Attention, which …