Open AccessA fusion CNN driven by images and vibration signals for fault diagnosis of gearbox
Author(s) -
Qiting Zhou,
Gang Mao,
Yongbo Li
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2252/1/012076
Subject(s) - robustness (evolution) , artificial intelligence , computer science , vibration , pattern recognition (psychology) , support vector machine , process (computing) , computer vision , fault (geology) , image fusion , fusion , image (mathematics) , acoustics , biochemistry , chemistry , physics , linguistics , philosophy , seismology , gene , geology , operating system
Gearbox diagnosis is critical for avoiding catastrophic failure and minimizing financial damages. Aiming at the problem that the vibration-based fault diagnosis methods cannot effectively identify the non-structural failure mode and the diagnosis model based on the infrared thermal image is not robust enough, a fusion fault diagnosis method for gearboxes using vibration signals and infrared images is proposed. By fusing these two kinds of heterogeneous data, the proposed method can identify both structural and unstructured health states while maintaining high robustness. In addition, CNN has powerful image processing capabilities, which can directly process two-dimensional infrared images and achieve high accuracy. Finally, a gearbox experiment is carried out to test the performance of our method. The results suggest that the proposed fusion CNN can obtain the highest accuracy compared with some methods based on single signals, shallow learning methods SVM and deep unsupervised learning methods SAE.