This study aims to develop an artificial intelligence-based model for analyzing the condition and detecting anomalies by encoding time-series data from manufacturing processes as images. Deep learning has demonstrated the significance of data analysis and anomaly detection in the vision field, and Convolutional Neural Networks (CNN) models have shown exceptional performance and high applicability in image analysis. Based on this, our study intends to utilize image encoding techniques to perform anomaly detection on time-series data. Data such as force, vibration, and sound from equipment during the manufacturing process are collected and transformed into images using various methods, including Gramian Difference Angular Field, Gramian Summation Angular Field, Markov Transition Field, and Recurrence Plot (RP). The transformed image data is then trained and classified for equipment conditions using various CNN models. Finally, we adopt the RP image encoding method and ResNet50 model, which demonstrated the highest accuracy of 99.6

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.

This study proposes a process for detecting anomalies in the manufacturing industry, where data imbalance is a frequent problem. The labeling of anomalies can be challenging owing to the different types of anomalies. To address this issue, we used clustering based on the distribution of acquired normal data. We extracted latent vector values from normal image data as features using the Style-GAN method, after conversion of the time-series data. Subsequently, we performed dimensionality reduction through Locally Linear Embedding (LLE) using the extracted latent vector values and selected the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for anomaly detection. We verified the proposed process using a milling dataset that included measurements of vibration, force, and noise. The evaluation of the process included dimensionality reduction methods such as Locally Linear Embedding (LLE), Principal Component Analysis (PCA), Kernel PCA, Singular Value Decomposition (SVD), and ISOmetric mapping (ISO) produced an F-1 score of 0.86.