Zhou Xianzhang, Li Aohan, Han Guangjie
Chongqing Academy of Education Science, Chongqing 400015, China.
Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo 1828585, Japan.
Sensors (Basel). 2023 Aug 31;23(17):7567. doi: 10.3390/s23177567.
It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.
准确诊断轴承故障对于避免工业中因电机故障造成的财产损失或人员伤亡至关重要。近年来，利用深度学习方法进行轴承故障诊断的方法以可靠且智能的方式提高了电机运行的安全性。然而，大多数工作主要适用于有足够轴承监测数据的情况。在工业系统中，由于监测条件恶劣以及一些特殊电机轴承信号的持续时间短，轴承传感器只能收集少量的监测数据。为了解决上述问题，本文引入了一种迁移学习策略，重点关注基于小样本融合的多局部模型轴承故障。该算法主要包括以下步骤：（1）构建一个并行的双向长短期记忆（Bi-LSTM）子网络，从工业电机轴承的振动和电流信号中提取特征，将提取的振动和电流信号特征进行串行融合以进行故障分类，并将其用作源域故障诊断模型；（2）使用最大均值差异算法测量源域轴承数据与目标轴承数据之间的分布差异；（3）基于源域和目标域之间的分布差异，迁移源域故障诊断模型的网络参数，微调源域故障诊断模型的网络结构，从而获得目标域故障诊断模型。性能评估表明，与其他方法相比，所提出的方法在小样本融合情况下能够保持更高的故障诊断准确率。此外，故障诊断模型的早期训练时间可以减少，其泛化能力可以在很大程度上得到提高。具体而言，使用所提出的方法，故障诊断准确率可以提高到80%以上，而训练时间可以减少到15.3%。
