… mechanical data in fault diagnosis. However, … ensemble Convolutional Neural Network
(ISECNN) is proposed in order to obtain a stable and well-performed DL based fault diagnosis …

… bearing fault diagnosis algorithm based on ensemble deep convolutional neural networks
and … The convolutional neural networks take the root mean square (RMS) maps from the FFT (…

… learning has the automatic feature extraction ability and ensemble … bearing fault diagnosis
method based on deep convolutional neural network (CNN) and random forest (RF) ensemble …

… Convolution neural networks (CNN) have shown high performance in feature extraction. …
, a novel bearing fault diagnosis model based on ensemble deep neural network (DNN) and …

… ensemble deep learning based fault diagnosis methods and learning rate selection for
deep learning. … (SECNN) is proposed for fault diagnosis in this research. However, as most …

… data and affect the diagnosis accuracy. To deal with … problem, this paper takes the
advantages of ensemble learning and proposes an ensemble convolutional neural network …

… a deep ensemble dense convolutional neural network (DEDCNN) is developed in this paper.
First, dense convolutional neural network (… ensemble learning to solve fault diagnosis tasks …

… However, the problem of information losses is always ignored during the fusion … above
problem, an ensemble convolutional neural network model is proposed for bearing fault diagnosis…

… data is still a great challenge in fault diagnosis. In this paper, a variant of … convolutional
neural network, named dynamic ensemble convolutional neural network was proposed for fault …

… and some features automatically extracted via Convolutional Neural Networks (CNNs). For
… is trained to diagnose the faults and their results are aggregated in an ensemble machine …