Compressive Sampling and Deep Neural Network (CS‐DNN)

The compressive sampling and sparse autoencoder‐based deep neural network (CS‐SAE‐DNN) uses CS for the sparse time‐frequency representation model to produce highly compressed vibration measurements from the high‐dimensional vibration data collected for the purpose of machine condition monitoring. This chapter presents an approach that has been proposed through the design of an intelligent fault‐classification method from highly compressed measurements using sparse‐overcomplete features and training a deep neural network through a sparse autoencoder (CS‐SAE‐DNN). This method includes the extraction of overcomplete sparse representations from highly compressed measurements. It involves unsupervised feature learning with a SAE algorithm for learning feature representations in multiple stages of nonlinear feature transformation based on a DNN. Case studies of bearing datasets are used to demonstrate how CS‐SAE‐DNN works and to validate its efficacy compared with other state‐of‐the‐art fault‐diagnosing techniques.