Deep neural networks-based rolling bearing fault diagnosis

Abstract

Rolling bearing is one of the most commonly used components in rotating machinery. It's so easy to be damaged that it can cause mechanical fault. Thus, it is significant to study fault diagnosis technology on rolling bearing. In this paper, three deep neural network models (Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders) are employed to identify the fault condition of rolling bearing. Four preprocessing schemes including feature of time domain, frequency domain and time-frequency domain are discussed. One data set with seven fault patterns is collected to evaluate the performance of deep learning models for rolling bearing fault diagnosis, which is based on the health condition of a rotating mechanical system. The results proved that the accuracy achieved by Deep Boltzmann Machines, Deep Belief Networks and Stacked Auto-Encoders are highly reliable and applicable in fault diagnosis of rolling bearing.