Fault diagnosis of rolling bearing based on tunable Q-Factor wavelet transform and convolutional neural

Abstract

The rolling bearing plays is used extensively in rotary machines and industrial processes. Effective fault diagnosis technology for a rolling bearing directly affects the life and operator safety of the device. In this paper, a fault diagnosis method based on a tunable-Q wavelet transform (TQWT) and a convolutional neural network (CNN) is proposed to reduce the influence of noise on the bearing vibration signal and to reduce the dependence on human experience in traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN extracts fault features and performs fault classification. Seven motor operating conditions-normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)-are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.