Zhao Chao, Liu Guokai, Shen Weiming
State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan 430074, China.
State Key Lab of Digital Manufacturing Equipment & Technology, Huazhong University of Science & Technology, Wuhan 430074, China.
ISA Trans. 2022 Nov;130:449-462. doi: 10.1016/j.isatra.2022.03.014. Epub 2022 Mar 16.
Domain adaptation techniques have attracted great attention in mechanical fault diagnosis. However, most existing methods work under the assumption that the source and target domains share the identical label space. Such methods are unable to handle a practical issue where the target label space is a subset of the source label space. To tackle this challenge, a balanced and weighted alignment network is proposed for partial transfer fault diagnosis. The proposed method views this issue from a new angle by augmenting the target domain to make the classes of two domains balanced and shortening class-center distances to reduce conditional distribution shifts. Meanwhile, a weighted adversarial alignment is developed to filter out the irrelative source samples and minimize marginal distribution discrepancy. As such, negative transfer can be avoided, and positive transfer can be enhanced. Comprehensive experiments on two test rigs demonstrate that the proposed method achieves promising performance and outperforms state-of-the-art partial transfer methods.
域适应技术在机械故障诊断中引起了广泛关注。然而，大多数现有方法是在源域和目标域共享相同标签空间的假设下工作的。这类方法无法处理目标标签空间是源标签空间子集的实际问题。为应对这一挑战，提出了一种用于部分迁移故障诊断的平衡加权对齐网络。该方法从一个新的角度看待这个问题，即通过扩充目标域使两个域的类别平衡，并缩短类中心距离以减少条件分布偏移。同时，开发了一种加权对抗对齐来滤除不相关的源样本并最小化边缘分布差异。这样，可以避免负迁移，并增强正迁移。在两个试验台上进行的综合实验表明，该方法取得了良好的性能，优于现有的部分迁移方法。
