… is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear
problems. This review will play an emphasis on convolutional neural network (CNN). The …

… fault diagnosis, where the testing data can not be obtained in advance. This paper proposes
a deep learning-based domain generalization method for machinery fault diagnosis. A …

… machinery, a novel fault diagnosis method based on Local-Global Deep Neural Network (…
First of all, this fault diagnosis method can directly use the proposed LGDNN algorithm to …

… We model these processes as a form of neural network and call it LCN. In this section, we
detail the construction of LCN for intelligent fault diagnosis using NSAE, namely NSAE-LCN. …

… that the fault feature information extracted by deep learning is … subjective selection of fault
feature index on fault diagnosis. … by deep learning was used in the final fault diagnosis, which …

… fault diagnosis of rotating machinery. Among these studies, the methods based on artificial
neural networks (… to ANNs for classifying faults. Though these methods did work in intelligent …

… fault diagnosis approach by integrating the continuous wavelet transform scalogram (CWTS)
[13] with a convolutional neural network (… for fault diagnosis to achieve better performance. …

… fault diagnosis of rotating machinery. In this paper, a novel impact feature extraction deep
neural network (IFE-DN) is proposed for intelligent gear and bearing fault diagnosis. An …

… transfer learning approach for fault detection in rotating machinery is proposed, inspired by
the success of transfer learning … Specifically, a transfer learning framework based on TCNN is …

… on gated recurrent unit recurrent neural network for fault diagnosis of rotating machinery (…
is twofold: (1) Improve the fault classification accuracy of rotating machinery. (2) Be robust to …