A CAE-Based Deep Learning Methodology for Rotating Machinery Fault Diagnosis

This paper deals with the development of a novel deep learning framework to achieve highly accurate rotating machinery fault diagnosis using residual wide-kernel deep convolutional auto-encoder. Unlike most deep learning methods, in which the input data was processed by fast Fourier transform (FFT) and wavelet transform, this paper aims to learn important features from the raw vibration signals. F...