FAULT DIAGNOSIS OF GEARBOX UNDER VARIABLE WORKING CONDITION BASED ON WEIGHTED SUBDOMAIN ADAPTIVE ADVERSARIAL NETWORK

In practical engineering, gearboxes are subject to complex and variable operating environments, which hinder the ability of a single vibration signal to accurately and effectively represent fault information under different working conditions. To address this issue, a gearbox fault diagnosis method...

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Bibliographic Details
Published inJi xie qiang du Vol. 47; pp. 96 - 103
Main Authors ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting
Format Journal Article
LanguageChinese
Published Editorial Office of Journal of Mechanical Strength 01.03.2025
Subjects
Data fusion
Domain adaptation
Fault diagnosis
Gearbox
Variable operating condition
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Summary:In practical engineering, gearboxes are subject to complex and variable operating environments, which hinder the ability of a single vibration signal to accurately and effectively represent fault information under different working conditions. To address this issue, a gearbox fault diagnosis method for variable working conditions based on weighted subdomain adaptive adversarial networks was proposed. Initially, a multi-source heterogeneous signal fusion strategy was employed to transform vibration signal spectrograms, current signal Gramian matrices, and infrared thermograms into a multi-channel dataset, offering diverse perspectives on gearbox operational states. Subsequently, a self-calibrated convolutions network (SCNet) incorporating an efficient channel attention (ECA) mechanism acted as a feature extractor, dynamically adjusting the interactions and dependencies between multi-source heterogeneous signals to balance the scale differences between the source and target domain heterogeneous data. Concurrent
ISSN:1001-9669