… in the form of a convolutional neural network model, which is an end-to-end machine learning
system. … By applying the convolutional neural network on this raw data, the network learns …

… of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a
few … is proposed involving the use of a convolutional neural network (CNN) to directly classify …

… diagnosis of rotating machinery. First, a Convolutional Neural Network (CNN)-based deep
learning … to classify various fault types of rotating machinery. The effectiveness of the proposed …

… in fault diagnosis of rotating machinery is still at the initial … on developing a convolutional
neural network to learn features … the one-dimensional convolutional neural network (1-DCNN) …

… This review will play an emphasis on convolutional neural network (… Deep neural networks:
A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery …

… of rotating machinery. Classic fault … rotating machinery fault diagnosis with feature extraction
algorithm based on empirical mode decomposition (EMD) and convolutional neural network …

… for rotating machinery fault diagnosis, two typical rotating machinery, bearings and
gearboxes, are investigated in this paper. Vibration signals of different machine conditions are …

… volutional neural network (NCNN) for effective identification … data, cost function of convolution
neural network (CNN) is … signals from source domain machine are obtained. Thereafter, …

… [10] developed an ensemble deep auto-encoder (EDA) method to learn discriminative
features of rotating machinery … Deep convolution neural network (DCNN) is a data-driven …

… fault diagnosis methods based on deep learning, convolutional neural network (CNN) has
… rotating machinery based on deep convolution neural network and support vector machine,’…