This study proposes a methodology for detecting anomalies in the manufacturing industry using a self-supervised representation learning approach based on deep generative models. The challenge arises from the limited availability of data on defective products compared with normal data, leading to degradation in the performance of deep learning models owing to data imbalances. To address this limitation, we propose a process that leverages the Gramian angular field to transform time-series data into images, applies StyleGAN for image augmentation of anomalous data, and utilizes a boosting algorithm for classifier selection in supervised learning. Additionally, we compared the accuracy of the classifier before and after data augmentation. In experimental cases involving CNC milling machine data and wire arc additive manufacturing data, the proposed approach outperformed the approach before augmentation, resulting in improved precision, recall, and F1-score for anomaly detection. Furthermore, Bayesian optimization of the hyperparameters of the boosting algorithm further enhanced the performance metrics. The proposed process effectively addresses the data imbalance problem, and demonstrates its applicability to various manufacturing industries.

本研究提出了一种使用基于深度生成模型的自监督表示学习方法来检测制造业异常的方法。与正常数据相比，缺陷产品的数据可用性有限，导致深度学习模型的性能因数据不平衡而下降。为了解决这个限制，我们提出了一种利用格拉米亚角场将时间序列数据转换为图像的过程，应用 StyleGAN 进行异常数据的图像增强，并利用增强算法在监督学习中进行分类器选择。此外，我们还比较了数据增强前后分类器的准确性。在涉及数控铣床数据和电弧增材制造数据的实验案例中，所提出的方法优于增强之前的方法，从而提高了异常检测的精度、召回率和 F1 分数。此外，Boosting 算法的超参数的贝叶斯优化进一步增强了性能指标。所提出的流程有效地解决了数据不平衡问题，并证明了其对各种制造行业的适用性。