Ensuring product quality in synthetic fiber manufacturing is critical to reducing defects and minimizing operational disruptions. This study evaluates three anomaly detection methods—rolling median, averaging-based signal fusion, and Principal Component Analysis (PCA)-based dimensionality reduction—applied to multivariate time-series data from fiber sensors monitoring properties such as magnitude, phase, node quality, and node count. Each method processes segmented 30-second windows and identifies anomalies using z-scores. Among the techniques, PCA-based fusion demonstrates superior performance by integrating multivariate signals, reducing noise, and improving detection accuracy. This approach offers a scalable, automated solution for fault detection, addressing the limitations of traditional heuristic-based methods. These findings underscore PCA’s potential to enhance quality control in fiber manufacturing, paving the way for more reliable anomaly detection in industrial applications.