Lee Daehwan, Jeong Jongpil
Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Republic of Korea.
Sensors (Basel). 2023 Jul 21;23(14):6587. doi: 10.3390/s23146587.
In this study, bearing fault diagnosis is performed with a small amount of data through few-shot learning. Recently, a fault diagnosis method based on deep learning has achieved promising results. Most studies required numerous training samples for fault diagnosis. However, at manufacturing sites, it is impossible to have enough training samples to represent all fault types under all operating conditions. In addition, most studies consider only accuracy, and models are complex and computationally expensive. Research that only considers accuracy is inefficient since manufacturing sites change rapidly. Therefore, in this study, we propose a few-shot learning model that can effectively learn with small data. In addition, a Depthwise Separable Convolution layer that can effectively reduce parameters is used together. In order to find an efficient model, the optimal hyperparameters were found by adjusting the number of blocks and hyperparameters, and by using a Depthwise Separable Convolution layer for the optimal hyperparameters, it showed higher accuracy and fewer parameters than the existing model.
在本研究中，通过少样本学习利用少量数据进行轴承故障诊断。最近，基于深度学习的故障诊断方法取得了有前景的成果。大多数研究需要大量训练样本进行故障诊断。然而，在制造现场，不可能有足够的训练样本以代表所有运行条件下的所有故障类型。此外，大多数研究仅考虑准确率，并且模型复杂且计算成本高。仅考虑准确率的研究效率低下，因为制造现场变化迅速。因此，在本研究中，我们提出了一种能够利用小数据有效学习的少样本学习模型。此外，还一起使用了能够有效减少参数的深度可分离卷积层。为了找到高效的模型，通过调整块数和超参数来找到最优超参数，并且对于最优超参数使用深度可分离卷积层，结果表明其比现有模型具有更高的准确率和更少的参数。
