Siamese multiscale residual feature fusion network for aero-engine bearing fault diagnosis under small-sample condition

Abstract

Implementing condition monitoring and fault diagnosis of aero-engine bearings is crucial to ensure that aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings are extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability, and (2) they do not mine fault information sufficiently or efficiently. This article proposes a Siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearing fault diagnosis under small-sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired according to the matching rules to realize the expansion of the sample size. Second, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small-sample diagnostic methods. The experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.