A novel two-stage fault detection and tracking method of hot-melt extrusion process based on data-driven techniques

Abstract In the hot-melt extrusion (HME) process, process parameters are critical in defining the quality of extruded products. Abnormal processing parameters may degrade product performance and lead to quality inconsistencies. Therefore, real-time monitoring of the HME process is crucial to ensuring product quality and consistency. However, most existing detection methods are constrained in their applicability owing to material-specific limitations. When process parameters vary slightly, the detection signals may exhibit minimal variations, thereby hindering the ability to differentiate between normal and abnormal conditions. To address these challenges, this paper proposes a novel two-stage fault detection and tracking method for the HME process based on data-driven techniques. A comprehensive material detection strategy is implemented by collecting and analyzing five types of data: near-infrared, Raman, ultrasound, melt pressure, and current. A global maximum variance-kernel principal component analysis anomaly detection model is constructed to identify four distinct abnormal conditions effectively. T 2 and squared prediction error statistical contribution maps are calculated to perform dimensionality reduction and extract features from the data. A conditional time reverse mapping-support vector machine anomaly localization model is developed that leverages the extracted features to establish the relationship between the five data types and specific abnormal conditions, thereby accurately pinpointing the root causes of anomalies. Experiments conducted using HME process equipment validate the proposed method. The results demonstrate that the two-stage fault detection and tracking approach effectively identifies various faults and achieves better recognition accuracy. This method provides a reliable solution for real-time monitoring and quality control in the HME process.