A Comparative Analysis of Machine Learning Algorithms for Detecting Spindle Anomaly in DTY Machine: Optimized Random Forest vs SVM

This study presents a comparative analysis of two machine learning
algorithms—Random Forest (RF) and Support Vector Machine (SVM)—for
detecting spindle anomalies using vibration signal features. A structured
workflow was implemented that involved signal acquisition, feature extraction,
preprocessing, and model training. To enhance classification accuracy, both
models were optimized using the Randomized Search CV method with 5-fold
cross-validation. Evaluation metrics included accuracy, precision, recall, F1-score,
confusion matrix, and the Receiver Operating Characteristic (ROC) curve analysis.
Before hyperparameter tuning, RF demonstrated superior performance
compared to SVM, particularly in abnormal condition detection. After the
optimization process, SVM exhibited substantial improvements across all metrics,
achieving an accuracy of 96% and a perfect Area Under the Curve (AUC) score of
1.00. In comparison, RF maintained stable, balanced performance, with an
accuracy of 94% and an AUC of 0.99. Training time analysis further revealed that
SVM is significantly faster, making it more suitable for real-time applications.
These results highlight the effectiveness of model tuning in improving fault
detection and demonstrate the potential of both RF and SVM as reliable tools for
predictive maintenance in DTY machines. Moreover, the findings offer practical
insights for implementing machine-learning-based solutions in industrial
vibration monitoring, particularly in the textile manufacturing sector.