关键词: 多源传感器数据, 故障诊断, 伪孪生神经网络, 星雀优化算法, 门控循环单元

Abstract: To address the demand for fault diagnosis of aero-engine bearings under extremely complex working conditions,a novel high-precision intelligent fault diagnosis method for aero-engine bearings was proposed.Initially,a dual-channel pseudo-siamese neural network was constructed,incorporating a one-dimensional adaptive batch normalization convolutional neural network module and a one-dimensional physics-guided convolutional neural network module,which could address integration of multi-source sensor data effectively,extract the key features and achieve the efficient fusion of diverse data sources.Subsequently,the gated recurrent unit optimized by nutcracker optimization algorithm was used to recognize fault state,enhancing the model's adaptability,robustness and generalization capability,especially under complex working conditions and noisy environments.Finally,the model's effectiveness was validated through case analysis,demonstrating fault diagnosis accuracy rates of 99.99%,100% and 100% respectively,with a maximum improvement of 23.97% in recognition accuracy compared to existing diagnostic methods,highlighting clear performance advantages.Additionally,generalization and noise resistance tests were conducted,and the results revealed superior noise robustness and strong generalization performance.

Key words: multi-source sensor data, fault diagnosis, pseudo-siamese neural network, nutcracker optimization algorithm, gated recurrent unit