{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:31:27Z","timestamp":1786509087546,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,9]],"date-time":"2026-08-09T00:00:00Z","timestamp":1786233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Minimizing unplanned downtime is critical for maintaining productivity in modern manufacturing. While combining sensor networks with machine learning provides a practical way to detect mechanical failures early, conventional data-driven diagnostics often fail during highly transient stamping operations. This failure stems from severe spectral smearing and signal distortions caused by fluctuating process loads and variable operating speeds. To address these limitations, we present a field-tested fault diagnosis (FD) framework deployed in an active automotive components plant. Over a twelve-month observation period, we collected raw vibration and process data from two operational transfer presses, building a comparative dataset that captures both localized gear damage and healthy baseline dynamics. After preprocessing the data to isolate signal anomalies, we systematically evaluated the diagnostic performance of six algorithms: SVM, Random Forest, Naive Bayes, k-NN, Decision Trees, and Logistic Regression. By integrating angle-based position data from a high-resolution encoder, the developed framework successfully pinpointed specific defective gear teeth. Ultimately, the Random Forest model outperformed the others, delivering the most robust detection accuracy under real-world factory conditions. These results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.<\/jats:p>","DOI":"10.3390\/s26165058","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T09:24:53Z","timestamp":1786353893000},"page":"5058","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3865-7054","authenticated-orcid":false,"given":"Ahmet Erdem","family":"Oner","sequence":"first","affiliation":[{"name":"TOYOTETSU Automotive Parts and Trading Joint Stock Company, 41420 Kocaeli, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8779-0476","authenticated-orcid":false,"given":"Meral","family":"Bayraktar","sequence":"additional","affiliation":[{"name":"Mechanical Engineering Department, Yildiz Technical University, 34349 Istanbul, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.procs.2022.01.318","article-title":"Predictive Maintenance on Sensorized Stamping Presses by Time Series Segmentation, Anomaly Detection and Classification Algorithms","volume":"200","author":"Coelho","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107735","DOI":"10.1016\/j.measurement.2020.107735","article-title":"Latest Developments in Gear Defect Diagnosis and Prognosis: A Review","volume":"158","author":"Kumar","year":"2020","journal-title":"Measurement"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yao, Y., Zhang, S., Yang, S., and Gui, G. (2020). Learning Attention Representation with a Multi-Scale CNN for Gear Fault Diagnosis under Different Working Conditions. Sensors, 20.","DOI":"10.3390\/s20041233"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1016\/j.measurement.2013.03.023","article-title":"A Fault Diagnosis Approach for Roller Bearing Based on VPMCD under Variable Speed Conditions","volume":"46","author":"Yang","year":"2013","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104798","DOI":"10.1016\/j.engfailanal.2020.104798","article-title":"Gear fault models and dynamics-based modelling for gear fault detection\u2014A review","volume":"117","author":"Mohammed","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"106889","DOI":"10.1016\/j.cie.2020.106889","article-title":"Predictive Maintenance in the Industry 4.0: A Systematic Literature Review","volume":"150","author":"Zonta","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nacchia, M., Fruggiero, F., Lambiase, A., and Bruton, K. (2021). A Systematic Mapping of the Advancing Use of Machine Learning Techniques for Predictive Maintenance in the Manufacturing Sector. Appl. Sci., 11.","DOI":"10.3390\/app11062546"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Tsallis, C., Papageorgas, P., Piromalis, D., and Munteanu, R.A. (2025). Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions. Appl. Sci., 15.","DOI":"10.3390\/app15094898"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Farooq, U., Ademola, M., and Shaalan, A. (2024). Comparative Analysis of Machine Learning Models for Predictive Maintenance of Ball Bearing Systems. Electronics, 13.","DOI":"10.3390\/electronics13020438"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Calabrese, M., Cimmino, M., Fiume, F., Manfrin, M., Romeo, L., Ceccacci, S., Paolanti, M., Toscano, G., Ciandrini, G., and Carrotta, A. (2020). SOPHIA: An Event-based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0. Information, 11.","DOI":"10.3390\/info11040202"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.future.2020.10.015","article-title":"FLAGS: A Methodology for Adaptive Anomaly Detection and Root Cause Analysis on Sensor Data Streams by Fusing Expert Knowledge with Machine Learning","volume":"116","author":"Steenwinckel","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1109\/TASE.2015.2468058","article-title":"An Automatic Process Monitoring Method Using Recurrence Plot in Progressive Stamping Processes","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.procs.2021.01.162","article-title":"High-quality Sheet Metal Production Using a Model-based Adaptive Approach","volume":"180","author":"Zehetner","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_14","unstructured":"Alpaydin, E. (2014). Introduction to Machine Learning, MIT Press. [3rd ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hwang, P.W., Chang, Y.J., Tsai, H.C., Tu, Y.T., and Yang, H.P. (2025). Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning Methodologies. Sensors, 25.","DOI":"10.3390\/s25061779"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"21918","DOI":"10.1109\/ACCESS.2017.2763172","article-title":"Time-frequency Analysis of Torsional Vibration Signals in Resonance Region for Planetary Gearbox Fault Diagnosis under Variable Speed Conditions","volume":"5","author":"Chen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1016\/j.jsv.2009.11.010","article-title":"A Wavelet Approach to Fault Diagnosis of a Gearbox under Varying Load Conditions","volume":"329","author":"Wang","year":"2010","journal-title":"J. Sound Vib."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vrba, J., Cejnek, M., Steinbach, J., and Krbcova, Z. (2021). A Machine Learning Approach for Gearbox System Fault Diagnosis. Entropy, 23.","DOI":"10.3390\/e23091130"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmad, B., and Forest, R. (2021). Intelligent Predictive Maintenance Model for Rolling Components of a Machine Based on Speed and Vibration. International Conference on Artificial Intelligence in Information and Communication (ICAIIC), IEEE.","DOI":"10.1109\/ICAIIC51459.2021.9415249"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106948","DOI":"10.1016\/j.cie.2020.106948","article-title":"The Experimental Application of Popular Machine Learning Algorithms on Predictive Maintenance and the Design of IIoT Based Condition Monitoring System","volume":"151","author":"Cakir","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"114598","DOI":"10.1016\/j.eswa.2021.114598","article-title":"Predictive Maintenance System for Production Lines in Manufacturing: A Machine Learning Approach Using IoT Data in Real-time","volume":"173","author":"Ayvaz","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"107267","DOI":"10.1016\/j.cie.2021.107267","article-title":"Intelligent Machine Learning Based Total Productive Maintenance Approach for Achieving Zero Down Time in Industrial Machinery","volume":"157","author":"Devaraj","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Matania, O., Klein, R., and Bortman, J. (2022). Transfer across Different Machines by Transfer Function Estimation. Front. Artif. Intell., 5.","DOI":"10.3389\/frai.2022.811073"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"110748","DOI":"10.1016\/j.ymssp.2023.110748","article-title":"A Novel Hybrid Physical AI-based Strategy for Fault Severity Estimation in Spur Gears with Zero-shot Learning","volume":"204","author":"Bachar","year":"2023","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4277","DOI":"10.1007\/s00170-021-08551-9","article-title":"Selecting an Appropriate Supervised Machine Learning Algorithm for Predictive Maintenance","volume":"119","author":"Ouadah","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ghahramani, Z. (2003). Unsupervised Learning. Advanced Lectures on Machine Learning, Springer.","DOI":"10.1007\/978-3-540-28650-9_5"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"114691","DOI":"10.1016\/j.engappai.2026.114691","article-title":"Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis","volume":"176","author":"Wang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"114577","DOI":"10.1016\/j.ymssp.2026.114577","article-title":"Application of machine-vision-driven physics-informed neural networks in pantograph\u2013catenary system state detection","volume":"257","author":"Yan","year":"2026","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1016\/j.gsf.2020.07.007","article-title":"Physics informed machine learning: Seismic wave equation","volume":"11","author":"Karimpouli","year":"2020","journal-title":"Geosci. Front."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1177\/1475921720972926","article-title":"A Review on Diagnostic and Prognostic Approaches for Gears","volume":"20","author":"Kundu","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"083301","DOI":"10.1115\/1.4043367","article-title":"A Step Toward Fault Type and Severity Characterization in Spur Gears","volume":"141","author":"Dadon","year":"2019","journal-title":"J. Mech. Des."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11831-016-9176-1","article-title":"Condition Monitoring Parameters for Fault Diagnosis of Fixed Axis Gearbox: A Review","volume":"24","author":"Goyal","year":"2016","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, Z., Jia, L., Kou, L., and Qin, Y. (2018). Spectral Kurtosis Entropy and Weighted SaE-ELM for Bogie Fault Diagnosis under Variable Conditions. Sensors, 18.","DOI":"10.3390\/s18061705"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.jsv.2016.09.005","article-title":"Fault Diagnosis of Gearbox Using Empirical Mode Decomposition and Multi-fractal Detrended Cross-correlation Analysis","volume":"385","author":"Liu","year":"2016","journal-title":"J. Sound Vib."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ymssp.2017.08.010","article-title":"An Integrated Method Based on CEEMD-SampEn and the Correlation Analysis Algorithm for the Fault Diagnosis of a Gearbox under Different Working Conditions","volume":"113","author":"Chen","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s12206-017-0107-3","article-title":"Gear Fault Diagnosis under Variable Conditions with Intrinsic Time-scale Decomposition-singular Value Decomposition and Support Vector Machine","volume":"31","author":"Xing","year":"2017","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_38","first-page":"112","article-title":"A Novel Knowledge Distillation Framework for Bearing Fault Diagnosis under Imbalanced Samples","volume":"25","author":"Cheng","year":"2026","journal-title":"Struct. Health Monit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5058\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:16:28Z","timestamp":1786508188000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5058"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,9]]},"references-count":38,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165058"],"URL":"https:\/\/doi.org\/10.3390\/s26165058","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,9]]}}}