MATEC Web Conf.
Volume 319, 20202020 8th Asia Conference on Mechanical and Materials Engineering (ACMME 2020)
|Number of page(s)||5|
|Section||Intelligent Manufacturing and Control Engineering|
|Published online||10 September 2020|
Domain Adaptation for Intelligent Fault Diagnosis under Different Working Conditions
Department of Electronic Engineering, Tsinghua University, Beijing, China.
* Corresponding author: email@example.com
Recently, deep learning algorithms have been widely into fault diagnosis in the intelligent manufacturing field. To tackle the transfer problem due to various working conditions and insufficient labeled samples, a conditional maximum mean discrepancy (CMMD) based domain adaptation method is proposed. Existing transfer approaches mainly focus on aligning the single representation distributions, which only contains partial feature information. Inspired by the Inception module, multi-representation domain adaptation is introduced to improve classification accuracy and generalization ability for cross-domain bearing fault diagnosis. And CMMD-based method is adopted to minimize the discrepancy between the source and the target. Finally, the unsupervised learning method with unlabeled target data can promote the practical application of the proposed algorithm. According to the experimental results on the standard dataset, the proposed method can effectively alleviate the domain shift problem.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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