Sparsity-Assisted Intelligent Condition Monitoring Method for Aero-engine Main Shaft Bearing

Weak feature extraction is of great importance for condition monitoring and intelligent diagnosis of aero-engine. Aimed at achieving intelligent diagnosis of aero-engine main shaft bearing, an enhanced sparsity-assisted intelligent condition monitoring method is proposed in this paper. Through analyzing the weakness of convex sparse model, i. e. the tradeoff between noise reduction and feature reconstruction, this paper proposes an enhanced-sparsity nonconvex regularized convex model based on Moreau envelope to achieve weak feature extraction. Accordingly, a sparsity-assisted deep convolutional variational autoencoders network is proposed, which achieves the intelligent identification of fault state through training denoised normal data. Finally, the effectiveness of the proposed method is verified through aero-engine bearing run-to-failure experiment. The comparison results show that the proposed method is good at abnormal pattern recognition, showing a good potential for weak fault intelligent diagnosis of aero-engine main shaft bearings.