He Binbei, Wang Huimin, Che Wei-Wei
College of Information Science and Engineering, Northeastern University, Shenyang, 110819, PR China.
ISA Trans. 2025 Jun;161:36-47. doi: 10.1016/j.isatra.2025.03.017. Epub 2025 Apr 3.
This paper investigates the machinery fault diagnosis problem in the case of multiple operating conditions. Considering that the mechanical equipment may experience different operating conditions during operation, a meta-domain-adversarial neural network (MDANN) is established for improving the adaptability of transfer models in multi-target-domain fault diagnosis, which utilizes meta-learning technique to view the multi-target-domain fault diagnosis as multiple tasks. In the data preprocessing stage, the continuous deep belief network is selected for outliers removal. Furthermore, to make the MDANN model available for the partial domain multi-target-domain fault diagnosis problem, a new group-wise comparison approach is proposed. Compared with the existing results, the proposed MDANN allows one trained fault diagnosis model to cope with different operating conditions, and it can be extended to address scenarios where the label spaces in the source and target domains are different. Finally, the proposed fault diagnosis method is experimented on the bearing datasets and compared with several state-of-the-art approaches, which proves its superiority and effectiveness.
本文研究了多工况情况下的机械设备故障诊断问题。考虑到机械设备在运行过程中可能会经历不同的工况，建立了一种元域对抗神经网络（MDANN），以提高迁移模型在多目标域故障诊断中的适应性，该网络利用元学习技术将多目标域故障诊断视为多个任务。在数据预处理阶段，选择连续深度信念网络来去除异常值。此外，为了使MDANN模型适用于部分域多目标域故障诊断问题，提出了一种新的分组比较方法。与现有结果相比，所提出的MDANN允许一个训练好的故障诊断模型应对不同的工况，并且可以扩展到解决源域和目标域标签空间不同的场景。最后，在所提出的故障诊断方法在轴承数据集上进行了实验，并与几种先进方法进行了比较，证明了其优越性和有效性。
