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

• Published in: Ji xie qiang du Vol. 47; pp. 96 - 103
• Main Authors: ZHANG Huiyun, ZUO Fangjun, YU Xi, YANG Ting
• Format: Journal Article
• Language: Chinese
• Published: Editorial Office of Journal of Mechanical Strength 01.03.2025
• Subjects: Data fusion; Domain adaptation; Fault diagnosis; Gearbox; Variable operating condition
• ISSN: 1001-9669

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