A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis
发布时间：2025-04-30
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- 发布时间：
- 2025-04-30
- 论文名称：
- A Novel Data Augmentation Method for Improving the Accuracy of Insulator Health Diagnosis
- 发表刊物：
- Sensors
- 摘要：
- Performing ultrasonic nondestructive testing experiments on insulators and then using machine learning algorithms to classify and identify the signals is an important way to achieve an intelligent diagnosis of insulators. However, in most cases, we can obtain only a limited number of data from the experiments, which is insufficient to meet the requirements for training an effective classification and recognition model. In this paper, we start with an existing data augmentation method called DBA (for dynamic time warping barycenter averaging) and propose a new data enhancement method called AWDBA (adaptive weighting DBA). We first validated the proposed method by synthesizing new data from insulator sample datasets. The results show that the AWDBA proposed in this study has significant advantages relative to DBA in terms of data enhancement. Then, we used AWDBA and two other data augmentation methods to synthetically generate new data on the original dataset of insulators. Moreover, we compared the performance of different machine learning algorithms for insulator health diagnosis on the dataset with and without data augmentation. In the SVM algorithm especially, we propose a new parameter optimization method based on GA (genetic algorithm). The final results show that the use of the data augmentation method can significantly improve the accuracy of insulator defect identification.
- 合写作者：
- Zhifeng Li, Yaqin Song*, Runchen Li, Sen Gu, Xuze Fan
- 卷号：
- 21
- 页面范围：
- 8187
- 是否译文：
- 否
- 发表时间：
- 2022-10-26