LJDA-Net: A Low-Rank Joint Domain Adaptation Network for Industrial Sample Enhancement

Abstract

The small sample problem is a challenge for data-driven industrial fault diagnosis. The available sample enhancement works have limited application in real industry, due to the difficulty in achieving the expected efficiency and accuracy performance of modeling. To address this issue, this paper proposes a low-rank joint domain adaptation network (LJDA-Net). First, we design the low-rank feature extraction subnet to effectively alleviate the limitation of feature redundancy and improve model training efficiency while ensuring feature accuracy. Second, we design the adaptively joint distribution alignment subnet, in which the internal multi-source domains and each source-target domain are jointly aligned, meanwhile, the correlation of each source-target domain is embedded in the alignment as the adaptive term, thus the performance of sample distribution alignment can be improved. By doing so, fault samples in other working conditions can be employed to enhance the small samples in current working conditions. Finally, benchmark simulated experiments and actual application experiments are conducted to evaluate the proposed method. All the results demonstrate that our LJDA-Net performs favorably against the state-of-the-art methods.