Choi Seung-Hwan, Park Jun-Kyu, An Dawn, Kim Chang-Hyun, Park Gunseok, Lee Inho, Lee Suwoong
Advanced Mechatronics Research Group, Daegyeong Division, Korea Institute of Industrial Technology, Daegu 42994, Republic of Korea.
Renewable Energy Solution Group, Korea Electric Power Research Institute (KEPRI), Naju 58277, Republic of Korea.
Sensors (Basel). 2023 Dec 11;23(24):9753. doi: 10.3390/s23249753.
This paper proposes fault diagnosis methods aimed at proactively preventing potential safety issues in robot systems, particularly human coexistence robots (HCRs) used in industrial environments. The data were collected from durability tests of the driving module for HCRs, gathering time-series vibration data until the module failed. In this study, to apply classification methods in the absence of post-failure data, the initial 50% of the collected data were designated as the normal section, and the data from the 10 h immediately preceding the failure were selected as the fault section. To generate additional data for the limited fault dataset, the Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) model was utilized and residual connections were added to the generator to maintain the basic structure while preventing the loss of key features of the data. Considering that the performance of image encoding techniques varies depending on the dataset type, this study applied and compared five image encoding methods and four CNN models to facilitate the selection of the most suitable algorithm. The time-series data were converted into image data using image encoding techniques including recurrence plot, Gramian angular field, Markov transition field, spectrogram, and scalogram. These images were then applied to CNN models, including VGGNet, GoogleNet, ResNet, and DenseNet, to calculate the accuracy of fault diagnosis and compare the performance of each model. The experimental results demonstrated significant improvements in diagnostic accuracy when employing the WGAN-GP model to generate fault data, and among the image encoding techniques and convolutional neural network models, spectrogram and DenseNet exhibited superior performance, respectively.
本文提出了旨在主动预防机器人系统中潜在安全问题的故障诊断方法，特别是工业环境中使用的人机协作机器人（HCR）。数据是从HCR驱动模块的耐久性测试中收集的，收集时间序列振动数据直至模块失效。在本研究中，为了在没有故障后数据的情况下应用分类方法，将收集到的数据的前50%指定为正常部分，并选择失效前10小时的数据作为故障部分。为了为有限的故障数据集生成额外的数据，利用了带有梯度惩罚的瓦瑟斯坦生成对抗网络（WGAN-GP）模型，并在生成器中添加了残差连接，以保持基本结构，同时防止数据关键特征的丢失。考虑到图像编码技术的性能因数据集类型而异，本研究应用并比较了五种图像编码方法和四种卷积神经网络模型，以促进最合适算法的选择。使用包括递归图、格拉姆角场、马尔可夫转移场、频谱图和小波尺度图在内的图像编码技术将时间序列数据转换为图像数据。然后将这些图像应用于包括VGGNet、GoogleNet、ResNet和DenseNet在内的卷积神经网络模型，以计算故障诊断的准确率并比较每个模型的性能。实验结果表明，使用WGAN-GP模型生成故障数据时诊断准确率有显著提高，在图像编码技术和卷积神经网络模型中，频谱图和DenseNet分别表现出优越的性能。
