… transferable convolutional neural network … deep model on target tasks by reusing the
pretrained network. Thus, the proposed method not only utilizes the learning power of deep network …

… an online fault diagnosis method based on a deep transfer convolutional neural network (…
dealing with different fault diagnosis tasks, the transferability of features in all layers of the …

… In this work, we focus on the inductive transfer learning for fault diagnosis tasks where … to
induce a transferred model for use in target tasks. Combined with the rise of deep learning, the …

… is indeed transferable towards gear fault diagnosis tasks and the proposed approach
performs well with raw image signal inputs, which indicates the transferred layers constructed in …

… one-dimensional (1-D) convolutional neural network (CNN) to automatically learn features
and … and transferring mid-level image representations using convolutional neural networks,” in …

… fault diagnosis system using wavelet packet energy as the input to a deep convolutional neural
network (CNN) … Lower-level network parameters are transferred from a previously trained …

… transferable neural network (STNN) is proposed for the intelligent machinery fault diagnosis
with … [27] proposed a generalization of deep neural network (DNN) using multiple kernels to …

… auto-encoder (CAE) is an auto-encoder which uses the convolutional neural network for
encoding and decoding, and it consists of an input layer, multiple hidden layers and an output …

… In order to achieve online fault diagnosis of rolling bearing effectively, this paper proposes
a rolling bearing fault diagnosis model based on online transfer convolutional neural network (…

… The application of feature transferring method in fault diagnosis is few. In this research,
the TCNN(ResNet-50) is designed by using ResNet-50 trained on ImageNet as the feature …