Fault anomaly detection method of aero-engine rolling bearing based on distillation learning.

In this study, we address the issue of limited generalization capabilities in intelligent diagnosis models caused by the lack of high-quality fault data samples for aero-engine rolling bearings. We provide a fault anomaly detection technique based on distillation learning to address this issue. Two Vision Transformer (ViT) models are specifically used in the distillation learning process, one of which serves as the teacher network and the other as the student network.

A New Dual-Input Deep Anomaly Detection Method for Early Faults Warning of Rolling Bearings.

To address the problem of low fault diagnosis accuracy caused by insufficient fault samples of rolling bearings, a dual-input deep anomaly detection method with zero fault samples is proposed for early fault warning of rolling bearings. First, the main framework of dual-input feature extraction based on a convolutional neural network (CNN) is established, and the two outputs of the main frame are subjected to the autoencoder structure. Then, the secondary feature extraction is performed.

Molecular Dynamics Simulation on Nanoindentation of M50 Bearing Steel.

M50 bearing steel has great potential for applications in the field of aerospace engineering, as it exhibits outstanding mechanical and physical properties. From a microscopic point of view, bearing wear originates from the microscopic region of the contact interface, which usually only contains hundreds or even several atomic layers. However, the existing researches seldom study the wear of M50 bearing steel on the microscopic scale.