Leakage-Controlled Classification of Extrusion Screw Condition Using Wavelet and Deep Learning Methods

Progressive extrusion-screw wear alters screw–material interaction and may become observable through process signals acquired during operation. This study compared raw-signal and continuous wavelet transform (CWT)-based pipelines while explicitly controlling temporal information leakage. Synchronized 1 Hz measurements of three motor phase currents and four barrel temperatures were recorded during two soybean-extrusion campaigns representing independently verified new and worn screw states. Five pipelines were evaluated using identical leakage-controlled temporal folds: time-domain descriptors with RBF-SVM, handcrafted CWT descriptors with RBF-SVM, raw-current 1D-CNN, CWT-scalogram 2D-CNN, and CWT-CNN with thermal-feature fusion. The raw-current 1D-CNN achieved the highest mean balanced accuracy (0.700 ± 0.075), whereas the CWT-based 2D-CNN produced the highest mean ROC-AUC (0.839 ± 0.160); however, the paired M3–M2 ROC-AUC confidence interval included zero. Handcrafted CWT descriptors underperformed conventional time-domain descriptors, and thermal fusion did not improve temporal generalization. The results therefore do not establish an inherent advantage of CWT. Because M2 and M3 differ in both representation and network architecture, their contrast is interpreted at pipeline level rather than as an isolated CWT effect. The evidence is limited to within-campaign temporal discrimination of the two recorded screw conditions and does not establish transferable wear diagnostics across independent campaigns.