A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis

The extraction of early fault features from time-series data is very crucial for convolutional neural networks (CNNs) in bearing fault diagnosis. To address this problem, a CNN framework based on identity mapping and Adam optimizer is presented for learning temporal dependencies and extracting fault... View More