School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Sensors (Basel). 2021 Dec 7;21(24):8168. doi: 10.3390/s21248168.
Aiming at the problem of fault diagnosis when there are only a few labeled samples in the large amount of data collected during the operation of rotating machinery, this paper proposes a fault diagnosis method based on knowledge transfer in deep learning. First, we describe the data collected during the operation as a two-dimensional image with both time and frequency-domain characteristics. Second, we transform the trained source domain model into a shallow model suitable for small samples in the target domain, and we train the shallow model with small samples with labels. Third, we input a large number of unlabeled samples into the shallow model, and the output result of the system is regarded as the label of the input sample. Fourth, we combine the original data and the data annotated by the shallow model to train the new deep CNN fault diagnosis model so as to realize the migration of knowledge from the expert system to the deep CNN. The newly built deep CNN model is used for the online fault diagnosis of rotating machinery. The FFCNN-SVM shallow model tagger method proposed in this paper compares the fault diagnosis results with other transfer learning methods at this stage, and its correct rate has been greatly improved. This method provides new ideas for future fault diagnosis under small samples.
针对旋转机械运行过程中采集到的大量数据中只有少量标记样本的故障诊断问题，本文提出了一种基于深度学习中知识迁移的故障诊断方法。首先，我们将运行过程中采集的数据描述为具有时间和频域特征的二维图像。其次，我们将训练好的源域模型转换为适合目标域小样本的浅层模型，并使用带有标签的小样本对浅层模型进行训练。然后，我们将大量未标记的样本输入到浅层模型中，系统的输出结果被视为输入样本的标签。最后，我们将原始数据和由浅层模型标注的数据结合起来，训练新的深度 CNN 故障诊断模型，从而实现从专家系统到深度 CNN 的知识迁移。新建立的深度 CNN 模型用于旋转机械的在线故障诊断。本文提出的 FFCNN-SVM 浅层模型标记方法与现阶段其他迁移学习方法进行了故障诊断结果比较，其准确率有了很大提高。该方法为未来小样本下的故障诊断提供了新的思路。
