… motors. These methods have promoted the application of multiscale CNN in fault diagnosis.
… fault-related features under strong noise interference. In addition, many studies use a …

… fault diagnosis framework called multiscale residual attention CNN (MRA-CNN) is proposed
to learn discriminative multiscale … network for motor fault diagnosis under nonstationary …

… that the size of the convolution kernel has an equal impact on the … researches on deep neural
network fault diagnosis based on multi… from the motor drive end of the test bench under the …

… than the classical convolutional neural networks, LeNet-5, … speed than the classical deep
neural networks. Furthermore, … The fault-sensitive frequency bands of signals differ under …

… bearing fault detection under strong noise environment, this paper proposed a novel anti-noise
deep residual multiscale convolutional neural network … Additionally, we design a residual …

… Xie, ‘‘Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox,’’ …
Multiscale kernel based residual convolutional neural network for motor fault diagnosis under …

… by bearing fault diagnosis experiments under variable working … fault features of bearings
and has strong robustness under … a fault diagnosis method for gearbox based on motor current …

… of CNN model for motor fault detection is 1D raw time … fault diagnosis of rolling bearings
under changeable operating conditions, a diagnosis model named Deep Convolutional Neural …

… sensing and multiscale inverted residual convolutional neural network (MIRCNN) is
proposed for fault diagnosis of … The multiscale neural network has been applied to motor fault …

… fault diagnosis methods may not be able to handle the motor … a global contextual residual
convolutional neural network (GC-… -ResCNN to explore multiscale and multilevel information of …