Fault domain separation networks: A cross-domain diagnostic method for variable operating conditions in the elevator guidance system

Abstract

High-speed elevators play a critical role in vertical transportation. However, they may exhibit different working conditions under varying operating conditions, posing challenges for fault diagnosis. We propose a model named fault domain separation networks (FDSN) to tackle the cross-domain diagnostic problem of guidance system by leveraging transfer learning techniques. FDSN comprises three distinct modules that extract invariant fault features and variable operating condition features, and use orthogonal loss function for feature decoupling, achieving effective feature allocation, and mitigating the impact of variable operating conditions on fault diagnosis. To evaluate and demonstrate the efficacy of FDSN, a high-speed elevator platform is constructed, and fault vibration data under various working conditions are simulated to form a comprehensive dataset. The proposed FDSN achieved an average diagnostic accuracy of 92.7% in cross-domain diagnostic tasks, outperforming other comparative methods and demonstrating the remarkable superiority.