Affiliation:
1. Department of Electrical Engineering National Taiwan University of Science and Technology Taipei City Taiwan
2. Department of Electrical Engineering Chung Yuan Christian University Taoyuan City Taiwan
Abstract
AbstractRecently, data‐driven cross‐domain fault diagnosis methods for rotating machinery have been successfully developed. However, most existing diagnostic methods assume that the label spaces of the source and target domains are the same. In practice, the relationship between the label space of the source domain and the target domain is unknown, that is, the universal domain adaptation (UDA) problem. Existing overall domain distribution alignment methods are less effective in facing UDA problems. Thus, this article proposes a deep learning‐based UDA model. First, the proposed model combines multi‐scale learning and dual attention block, which can improve the capability to extract effective features. Then, an entropy optimization strategy is introduced to promote target domain sample clustering without prior knowledge. Finally, the effectiveness of the proposed model is verified on a public dataset of rotating machinery. The results show that the proposed method outperforms six existing cross‐domain fault diagnosis methods.
Funder
National Science and Technology Council
Publisher
Institution of Engineering and Technology (IET)
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