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 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 transfer learning. 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.

Multiscale permutation entropy gray image coding method and its application in bearing fault diagnosis

“…Regrettably, harsh working conditions and a significant amount of background noise [2], in signals have made the bearing structure particularly susceptible to fatigue fracture from multiple loads and demanding working conditions [3]. The running state of bearings has a direct impact on the effectiveness and safety of mechanical equipment [4]. Timely detection of bearing faults is therefore crucial [5].…”