SeqInfo-SAWGAN-GP: Adaptive feature extraction from vibration time data under variable operating conditions for imbalanced bearing fault diagnosis
Document Type
Article
Publication Date
2-1-2025
Abstract
Current generative adversarial network (GAN)-based methods are widely used to address data imbalance in intelligent fault diagnosis of bearings, a problem often caused by the scarcity of faulty data in real-world scenarios. However, these methods still face challenges, including high model training difficulty and poor quality under variable operating conditions. To address this, sequence information incorporated self-attention Wasserstein generative adversarial network with gradient penalty (SeqInfo-SAWGAN-GP) is proposed. Firstly, a self- attention block is integrated to extract significant features from limited time data. Then, SeqInfo block is introduced to enhance the quality of synthetic time data and manage the model to generate data across different operating conditions. Additionally, healthy vibration signals corresponding to specific operating conditions are input into the generator to further expedite the generation of synthetic fault data. Finally, the superiority of the proposed method is validated on two bearing datasets. The results indicate that the proposed method achieves accuracy of 98.5% and 95% using only a few samples, marking improvements of 4% and 45% over the original imbalanced dataset on two bearing datasets under variable operating conditions, respectively, and outperforming state-of-the-art methods.
Keywords
Data imbalance, Generative adversarial network, Self-attention, Variable operating conditions, Fault diagnosis
Divisions
fac_eng,mechanical
Funders
Ministry of Higher Education for the Fundamental Research (FRGS/1/2022/TK10/UM/02/29),UM Matching (MG010-2024),Advanced Shock and Vibration Research (ASVR) Group of Universiti Malaya
Publication Title
Measurement
Volume
243
Publisher
Elsevier
Publisher Location
125 London Wall, London, ENGLAND