Chu Liuxing, Li Qi, Yang Bingru, Chen Liang, Shen Changqing, Wang Dong
School of Mechanical and Electric Engineering, Soochow University, Suzhou, 215000, China.
The State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, 200240, China.
Heliyon. 2023 Mar 11;9(3):e14545. doi: 10.1016/j.heliyon.2023.e14545. eCollection 2023 Mar.
Compound fault diagnosis in essence is a fundamental but difficult problem to be solved. The separation and extraction of compound fault features remain great challenges in industrial applications due to the lack of labeled fault data. This paper proposes a novel multi-label domain adaptation method applicable to compound fault diagnosis of bearings. Firstly, multi-layer domain adaptation is designed based on a fault feature extractor with customized residual blocks. In that way, features from discrepant domain can be transformed into domain-invariant features. Furthermore, a multi-label classifier is applied to decompose compound fault features into corresponding single fault feature, and diagnoses them separately. The application on bearing datasets demonstrates that the proposed method could enhance the detachable degree of compound faults and achieve greater diagnostic performance than other existing methods.
复合故障诊断本质上是一个有待解决的基础性难题。由于缺乏带标签的故障数据，复合故障特征的分离与提取在工业应用中仍然是巨大的挑战。本文提出了一种适用于轴承复合故障诊断的新型多标签域自适应方法。首先，基于具有定制残差块的故障特征提取器设计了多层域自适应。通过这种方式，来自不同域的特征可以转换为域不变特征。此外，应用多标签分类器将复合故障特征分解为相应的单一故障特征，并分别进行诊断。在轴承数据集上的应用表明，所提出的方法可以提高复合故障的可分离程度，并比其他现有方法具有更好的诊断性能。
