Search

Sufficient labeled fault samples are the key to ensuring the performance of deep learning diagnostic models. However, in practical engineering applications, machinery and equipment operate normally most of the time, and it is difficult to collect enough fault data. In contrast, through the kinetics analysis method, simulation data sets of various fault types can be easily obtained. However, there is a difference in distribution between simulation data and real data. The deep learning diagnosis model trained directly with simulation data lacks versatility and cannot be applied to fault diagnosis of real data. To this end, this paper proposes a simulation datadriven deep transfer learning fault diagnosis method, which applies the fault diagnosis knowledge in the simulation data to the real data fault diagnosis task. The effectiveness of the method is verified by experiments on the two-stage planetary gearbox in the Drivetrain Diagnostics Simulator (DDS) test bench. [ABSTRACT FROM AUTHOR]

This article proposes a novel lightweight attention spatiotemporal joint distribution adaptation network fault diagnosis model to address the key challenges of domain transfer and high model complexity in traditional methods. The novelty lies in 1. Using model compression techniques to reduce the complexity of the network model and improve its computational efficiency; 2. Introducing new domain adaptation and adversarial methods to solve the domain transfer problem. The effectiveness of the proposed model is verified through a transfer experiment of planetary gearbox vibration data. The experimental results show that the proposed model reduces the parameters and computational complexity to 18 % and 15 % of the original model, respectively, and has a diagnostic accuracy of over 98 % in cross-condition transfer tasks, and still maintains an accuracy of over 88 % even under high noise levels. This indicates that the proposed model is an efficient and accurate fault diagnosis model. [ABSTRACT FROM AUTHOR]