… 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 …

… 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 …

… 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 …

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

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

… 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 …

… A deep convolutional neural network with new training methods for bearing fault diagnosis
under noisy environment and different working load[J]. Mechanical Systems & Signal …

… The initial attempts of applying support vector machines (SVMs) to fault diagnosis were in
the late 1990s [6]. Fuzzy logic (FL) can partition the feature space into fuzzy sets and utilize …