School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing 210038, China.
Sensors (Basel). 2021 May 12;21(10):3382. doi: 10.3390/s21103382.
As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The 2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.
作为传递动力和扭矩的关键组件，旋转机械的故障诊断对于保证机械设备的可靠运行至关重要。遗憾的是，样本类不平衡是工业应用中的常见现象，这导致了域自适应（DA）的大跨域分布差异，并导致大多数现有的机械故障诊断方法的性能下降。为了解决这个问题，提出了一种新的 DA 方法，该方法同时减小了跨域分布差异和几何差异，定义为 MRMI。这项工作包含三个部分来改善样本类不平衡问题：（1）提出了一种新的距离度量方法（MVD），并应用于改进边缘分布自适应的性能。（2）流形正则化与实例重新加权相结合，以同时探索内在流形结构并自适应地去除不相关的源域样本。（3）2-范数正则化作为数据预处理工具，以提高模型的泛化性能。应用具有类不平衡样本的齿轮和滚动轴承数据集来验证 MRMI 的可靠性。根据故障诊断结果，在样本类不平衡的情况下，MRMI 可以显著优于竞争方法。
