Abstract: The development of modern industry demands factories to operate under a flexible production mode involving multi-variety and mixed small-to-large batch sizes. It requires precise real-time quality monitoring methods to address the risk of quality fluctuations due to frequent process adjustments, ensuring consistent product quality. This study aims to integrate statistical process control (SPC) for monitoring quality anomalies in processes with unknown parameter distributions. A non-parametric GWMA SR control chart is developed for real-time monitoring of quality parameters. To achieve anomaly recognition, a PCA-RF-GA model is proposed for feature selection and fusion. Feature dimensionality is reduced by principal component analysis (PCA), and a random forest (RF) algorithm optimized by genetic algorithm (GA) is proposed for pattern classification. Simulation tests compare the recognition speed and accuracy across feature combinations and classification methods, demonstrating that the proposed method achieves superior performance. The proposed method is further applied to UO2 pellet diameter monitoring as an actual example. Results show that the combination of GWMA SR control chart and PCA-RF-GA model effectively identifies abnormal quality fluctuations in actual production with unknown parameter distributions.