Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines
Sensors, cilt.26, sa.16, ss.1-31, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 26 Sayı: 16
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/s26165058
- Dergi Adı: Sensors
- Derginin Tarandığı İndeksler: Scopus
- Sayfa Sayıları: ss.1-31
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Modern industrial operations heavily rely on minimizing unplanned downtime to safeguard productivity boundaries. Within this context, Predictive Maintenance (PdM) offers a viable track to intercept mechanical failures early by marrying sensor networks with machine learning tools. In this study, we introduce a field-tested PdM framework deployed in an automotive components plant. Over a twelve-month observation window, we captured raw vibration and process parameters from two operational transfer presses to build a comparative dataset containing both localized gear damage and intact baseline dynamics. Following a data-purging phase to isolate signal anomalies, we systematically tested the diagnostic limits of six algorithms, including SVM, Random Forest, Naive Bayes, k-NN, Decision Trees, and Logistic Regression. Ultimately, Random Forest and k-Nearest Neighbor frameworks stood out, yielding the most robust detection accuracy under real-world factory constraints. These findings demonstrate that the proposed PdM approach significantly reduces resource waste and prevents unnecessary downtime, offering a scalable model for industrial asset management. Furthermore, angle-based position information was acquired via an integrated high-resolution encoder, enabling the developed software to detect specific teeth exhibiting defects.