Intelligent fault diagnosis for variable working conditions of rotor-bearing system based on vibration image and domain adaptation

Abstract In recent years, intelligent condition monitoring and diagnosis based on deep learning have made great progress. However, traditional diagnostic methods mostly perform vibration analysis based on accelerometer signals, ignoring the influence of sensors on the mass load of the measured object. On the other hand, conventional transfer learning (TL) methods are mostly based on global distribution alignment to achieve intelligent diagnosis under variable working conditions. In this paper, a deep global subdomain adaptation network (DGSAN) is proposed to solve the intelligent diagnosis problem under variable working conditions based on vibration image and TL. First, visual measurement is introduced in vibration extraction. Based on the phase vibration extraction method, the vibration feature information is obtained from the visual vibration image to construct the vibration dataset. Then, the proposed DGSAN establishes a multi-layer domain adaptive network to minimize the difference in feature distribution and realize fine-grained feature distribution alignment of fault data under variable working conditions. Comparative experiments are carried out on the vibration image datasets of rotor-bearing systems, and the results show that the proposed method achieves high-precision transfer intelligent diagnosis.