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

… model for condition monitoring based on convolutional neural networks. The goal of this …
fault detection from the data itself. Several types of bearing faults such as outer-raceway faults …

… 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 …

… 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 …

… , machine fault diagnosis has become increasingly significant to ensure safe equipment
operation and … Convolutional neural network (CNN), as a typical representative of intelligent …

… feature sets are not yet able to respond adequately to the complex task of fault diagnosis.
To address this, a fault diagnosis method, based on deep convolution neural network (DCNN) …

… problem of end-to-end fault diagnosis, this paper focuses on developing a convolutional
neural network … the original vibration signals and then diagnose faults. The effectiveness of the …

… a novel fault diagnosis method to achieve the fault diagnosis of rotating machinery. First, a
… This section focuses on the IRT-based fault diagnosis of rotating machinery and the state-of-…

… Classic fault diagnosis methods are mainly … machinery fault diagnosis with feature extraction
algorithm based on empirical mode decomposition (EMD) and convolutional neural network …

… to the automatic and accurate fault diagnosis. In this case, this paper proposes a … convolutional
neural network (LCNN) method for intelligent fault diagnosis of rotating machinery, which …