Unsupervised Anomaly Detection Process Using LLE and HDBSCAN by Style-GAN as a Feature Extractor

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.