A multi convolution pooling group fault diagnosis model with high generalization across data sets and large receptive field characteristics considering industrial environmental noise

Author:

Pan WujiuORCID,Cao Shuming,Xu Liang,Sun YingHao,Nie Peng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province of China

Scientific Research Fund of Liaoning Education Department

Publisher

Springer Science and Business Media LLC

Reference57 articles.

1. Pan WJ, Ling LY, Qu HY, Wang MH (2022) Coupling dynamic behavior of aero-engine rotor system caused by rolling, pitching and yawing maneuver loads. Appl Math Model 102:726–747

2. Pan WJ, Ling LY, Qu HY (2023) Dynamic modeling and response analysis of rub-impact rotor system with squeeze film damper under maneuvering load. Appl Math Model 114:544–582

3. Pan WJ, Qu HY, Sun YH (2023) A deep convolutional neural network model with two-stream feature fusion and cross-load adaptive characteristics for fault diagnosis. Meas Sci Tecgnol 34:1–23

4. Pan WJ, Sun YH, Cheng RR, Cao SM (2023) A SENet-TSCNN model developed for fault diagnosis considering squeeze-excitation networks and two-stream feature fusion. Meas Sci Technol 34:1–21

5. Cerrada M, Sanchez RV, Li C, Pacheco F, Cabrera D, Oliverira JVD, Vasquez RE (2018) A review on data-driven fault severity assessment in rolling bearings. Mech Syst Signal 99:169–196

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