Zheng Xiaorong, Gu Zhaojian, Liu Caiming, Jiang Jiahao, He Zhiwei, Gao Mingyu
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China.
Entropy (Basel). 2022 Aug 15;24(8):1122. doi: 10.3390/e24081122.
Domain adaptation-based bearing fault diagnosis methods have recently received high attention. However, the extracted features in these methods fail to adequately represent fault information due to the versatility of the work scenario. Moreover, most existing adaptive methods attempt to align the feature space of domains by calculating the sum of marginal distribution distance and conditional distribution distance, without considering variable cross-domain diagnostic scenarios that provide significant cues for fault diagnosis. To address the above problems, we propose a deep convolutional multi-space dynamic distribution adaptation (DCMSDA) model, which consists of two core components: two feature extraction modules and a dynamic distribution adaptation module. Technically, a multi-space structure is proposed in the feature extraction module to fully extract fault features of the marginal distribution and conditional distribution. In addition, the dynamic distribution adaptation module utilizes different metrics to capture distribution discrepancies, as well as an adaptive coefficient to dynamically measure the alignment proportion in complex cross-domain scenarios. This study compares our method with other advanced methods, in detail. The experimental results show that the proposed method has excellent diagnosis performance and generalization performance. Furthermore, the results further demonstrate the effectiveness of each transfer module proposed in our model.
基于域自适应的轴承故障诊断方法近年来受到了高度关注。然而，由于工作场景的多样性，这些方法中提取的特征未能充分表示故障信息。此外，大多数现有的自适应方法试图通过计算边缘分布距离和条件分布距离之和来对齐域的特征空间，而没有考虑为故障诊断提供重要线索的可变跨域诊断场景。为了解决上述问题，我们提出了一种深度卷积多空间动态分布自适应（DCMSDA）模型，该模型由两个核心组件组成：两个特征提取模块和一个动态分布自适应模块。从技术上讲，在特征提取模块中提出了一种多空间结构，以充分提取边缘分布和条件分布的故障特征。此外，动态分布自适应模块利用不同的度量来捕获分布差异，以及一个自适应系数来动态测量复杂跨域场景中的对齐比例。本研究详细地将我们的方法与其他先进方法进行了比较。实验结果表明，该方法具有优异的诊断性能和泛化性能。此外，结果进一步证明了我们模型中提出的每个迁移模块的有效性。
