{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T20:10:30Z","timestamp":1774642230358,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Sustainability can be achieved through the widespread adoption of electrification across multiple sectors of activity, which would thereby enable increased operational efficiency and reduce the environmental impact. The attainment of this purpose relies on electrical circuits that convert electrical energy from renewable power plants into forms that are compatible with the specific requirements of the load. Failure of the aforementioned circuits, denominated as power converters, can lead to financial losses resulting from unexpected shutdowns and, in critical systems, can pose significant risks to human life. This article focuses on the topic of fault diagnosis in power converters. Some of the most vulnerable components of these converters are the capacitors used in the DC-link, whose failure evolves gradually. When the capacitor internal resistance (ESR) or the capacitor capacitance (C) exceeds a certain threshold value, it is advisable to propose a system shutdown, as soon as possible, to replace the capacitor. The solution presented in this article combines signal processing techniques (SPTs) with a machine learning (ML) algorithm to determine the optimal time for capacitor replacement. The ML algorithm employed herein was a logistic regression (LR) algorithm which classified the capacitor into one of two states: normal operation (0) or failure (1). To train and evaluate the LR model, two different datasets were created using various electrical quantities that can be measured non-invasively. The model demonstrated excellent performance, achieving an accuracy, precision, recall, and F1 score above 0.99.<\/jats:p>","DOI":"10.3390\/app15136971","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T11:23:24Z","timestamp":1750418604000},"page":"6971","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Enhancing Power Converter Reliability Through a Logistic Regression-Based Non-Invasive Fault Diagnosis Technique"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8025-6898","authenticated-orcid":false,"given":"Ac\u00e1cio M. R.","family":"Amaral","sequence":"first","affiliation":[{"name":"Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, P\u20133030-199 Coimbra, Portugal"},{"name":"CISE-Electromechatronic Systems Research Centre, University of Beira Interior, P\u20136201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","unstructured":"Eurostat (2025, May 02). Energy Statistics\u2014An Overview. Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=Energy_statistics_-_an_overview."},{"key":"ref_2","unstructured":"(2024). Electricity 2024-Analysis and Forecast to 2026, IEA Publications."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164520","DOI":"10.1109\/ACCESS.2021.3135037","article-title":"Electrification of Agricultural Machinery: A Review","volume":"9","author":"Scolaro","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"144456","DOI":"10.1109\/ACCESS.2024.3471647","article-title":"Smart Agriculture: Current State, Opportunities, and Challenges","volume":"12","author":"Ahmed","year":"2024","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"17880","DOI":"10.1109\/ACCESS.2018.2820326","article-title":"Underwater-Drone with Panoramic Camera for Automatic Fish Recognition Based on Deep Learning","volume":"6","author":"Meng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_6","unstructured":"Industrial Electrification (2025, May 02). International Institute for Sustainable Development. Available online: https:\/\/www.iisd.org\/system\/files\/2022-05\/industrial-electrification-en.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"48017","DOI":"10.1109\/ACCESS.2024.3379138","article-title":"Electrifying Urban Transportation: A Comparative Study of Battery Swap Stations and Charging Infrastructure for Taxis in Chicago","volume":"12","author":"Borgosano","year":"2024","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2087","DOI":"10.1109\/TLA.2022.9853229","article-title":"Introducing Electric Bus Fleets in Rio de Janeiro City Methodology and Analysis","volume":"20","author":"Silva","year":"2022","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1104","DOI":"10.1109\/TMRB.2024.3421635","article-title":"A Digital Twin-Based Large-Area Robot Skin System for Safer Human-Centered Healthcare Robots Toward Healthcare 4.0","volume":"6","author":"Yang","year":"2024","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9270","DOI":"10.1109\/TEM.2023.3327069","article-title":"RubikCell: Toward Robotic Cellular Warehousing Systems for E-Commerce Logistics","volume":"71","author":"Ma","year":"2024","journal-title":"IEEE Trans. Eng. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MPEL.2018.2874345","article-title":"Failure Analysis of the dc-dc Converter: A Comprehensive Survey of Faults and Solutions for Improving Reliability","volume":"5","author":"Costa","year":"2018","journal-title":"IEEE Power Electron. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"195505","DOI":"10.1109\/ACCESS.2024.3518516","article-title":"Assessment of IoT-Driven Predictive Maintenance Strategies for Computed Tomography Equipment: A Machine Learning Approach","volume":"12","author":"Azrul","year":"2024","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"13432","DOI":"10.1109\/ACCESS.2023.3242918","article-title":"Fault Management Techniques to Enhance the Reliability of Power Electronic Converters: An Overview","volume":"11","author":"SRahimpour","year":"2023","journal-title":"IEEE Access"},{"key":"ref_14","unstructured":"Cardoso, A.J.M. (2018). Diagnosis and Fault Tolerance of Electrical Machines, Power Electronics and Drives, The Institution of Engineering and Technology."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2697","DOI":"10.1109\/TIM.2008.925013","article-title":"An Economic Offline Technique for Estimating the Equivalent Circuit of Aluminum Electrolytic Capacitors","volume":"57","author":"Amaral","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TPEL.2017.2736162","article-title":"Quasi-Online Technique for Health Monitoring of Capacitor in Single-Phase Solar Inverter","volume":"33","author":"Agarwal","year":"2018","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., Laadjal, K., and Cardoso, A.J.M. (2024, January 10\u201312). Advancements in Fault Diagnosis Techniques for Aluminum Capacitors Using STLSP and Autoencoder. Proceedings of the IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE), Yasmine Hammame, Tunisia.","DOI":"10.1109\/ICAIGE62696.2024.10776613"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques\u2014Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Amaral, A., Laadjal, K., and Cardoso, A.J.M. (2024). Optimized Preventive Diagnostic Algorithm for Assessing Aluminum Electrolytic Capacitor Condition Using Discrete Wavelet Transform and Kalman Filter. Electronics, 13.","DOI":"10.3390\/electronics13163265"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1049\/iet-pel.2019.0672","article-title":"Review on fault-diagnosis and fault-tolerance for DC\u2013DC converters","volume":"13","author":"Kumar","year":"2020","journal-title":"IET Power Electron."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., and Cardoso, A.J.M. (2022). Simulation Tool to Evaluate Fault Diagnosis Techniques for DC-DC Converters. Symmetry, 14.","DOI":"10.3390\/sym14091886"},{"key":"ref_22","unstructured":"Nichicon Corporation (2025, April 18). General Descriptions of Aluminum Electrolytic Capacitors-Technical Notes CAT.8101E-1. Available online: https:\/\/www.nichicon.co.jp\/english\/products\/pdf\/aluminum-e.pdf."},{"key":"ref_23","unstructured":"Guide, S. (2024). Technical Guide: Aluminum Electrolytic Capacitor, Conductive Polymer Hybrid Aluminum and Electrolytic Capacitor, Panasonic Industry."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MEI.2010.5383924","article-title":"Historical introduction to capacitor technology","volume":"26","author":"Ho","year":"2010","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_25","unstructured":"Amaral, A.M.R., Laadjal, A.K., and Cardoso, J.M. (October, January 30). Assessment of Aluminum Electrolytic Capacitors Health Status Through Signal-Based Techniques. Proceedings of the IEEE 21st International Power Electronics and Motion Control Conference (PEMC), Pilsen, Czech Republic."},{"key":"ref_26","unstructured":"Dubilier, C.C. (2025, May 01). Aluminum Electrolytic Capacitor Application Guide. Available online: https:\/\/www.cde.com\/resources\/technical-papers\/KNO_CD_AEappGuide_R2.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1109\/63.261004","article-title":"Use of ESR for deterioration diagnosis of electrolytic capacitor","volume":"8","author":"Harada","year":"1993","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., and Cardoso, A.J.M. (2004, January 21\u201323). Fault diagnosis on switch-mode power supplies operating in discontinuous mode. Proceedings of the Second International Conference on Power Electronics, Machines and Drives (PEMD 2004), Edinburgh, UK.","DOI":"10.1049\/cp:20040284"},{"key":"ref_29","unstructured":"Amaral, A.M.R., and Cardoso, A.J.M. (September, January 31). Using input current and output voltage ripple to estimate the output filter condition of switch mode DC\/DC converters. Proceedings of the IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, Cargese, France."},{"key":"ref_30","unstructured":"Aeloiza, E., Kim, J.-H., Enjeti, P., and Ruminot, P. (2005, January 12\u201316). A Real Time Method to Estimate Electrolytic Capacitor Condition in PWM Adjustable Speed Drives and Uninterruptible Power Supplies. Proceedings of the IEEE 36th Power Electronics Specialists Conference, Dresden, Germany."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/2943.974353","article-title":"Realization of a smart electrolytic capacitor circuit","volume":"8","author":"Venet","year":"2002","journal-title":"IEEE Ind. Appl. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., and Cardoso, A.J.M. (2009, January 3\u20135). State condition estimation of aluminum electrolytic capacitors used on the primary side of ATX power supplies. Proceedings of the 35th Annual Conference of IEEE Industrial Electronics, Porto, Portugal.","DOI":"10.1109\/IECON.2009.5414963"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6386","DOI":"10.1109\/TPEL.2019.2951859","article-title":"Condition Monitoring of DC-Link Capacitors Using Goertzel Algorithm for Failure Precursor Parameter and Temperature Estimation","volume":"35","author":"Sundararajan","year":"2020","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_34","unstructured":"Amaral, A.M.R., Laadjal, K., and Cardoso, A.J.M. (October, January 30). Enhanced DC-link Capacitors Failure Diagnosis for a Three-Phase Interleaved Converter, Using Hilbert Transform. Proceedings of the IEEE 21st International Power Electronics and Motion Control Conference (PEMC), Pilsen, Czech Republic."},{"key":"ref_35","unstructured":"Wang, G., Guan, Y., Zhang, J., Wu, L., Zheng, X., and Pan, W. (2012, January 23\u201325). ESR estimation method for DC-DC converters based on improved EMD algorithm. Proceedings of the IEEE 2012 Prognostics and System Health Management Conference, Beijing, China."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, Z., Lin, Q., Yu, K., Su, X., and Du, W. (2022, January 27\u201330). A Non-invasive Online ESR Estimating Method for DC-Link Capacitors of UPS. Proceedings of the 4th International Conference on Smart Power & Internet Energy Systems (SPIES), Beijing, China.","DOI":"10.1109\/SPIES55999.2022.10082405"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10153","DOI":"10.1109\/TPEL.2018.2890617","article-title":"An Online ESR Estimation Method for Output Capacitor of Boost Converter","volume":"34","author":"Ren","year":"2019","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_38","unstructured":"Ma, H., Mao, X., Zhang, N., and Xu, D. (2005, January 12\u201316). Parameter Identification of Power Electronic Circuits Based on Hybrid Model. Proceedings of the IEEE 36th Power Electronics Specialists Conference, Recife, Brazil."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Buiatti, G.M., Amaral, A.M.R., and Cardoso, A.J.M. (2007, January 23\u201327). ESR Estimation Method for DC\/DC Converters Through Simplified Regression Models. Proceedings of the IEEE Industry Applications Annual Meeting, New Orleans, LA, USA.","DOI":"10.1109\/IAS.2007.346"},{"key":"ref_40","unstructured":"Buiatti, G.M., Amaral, A.M.R., and Cardoso, A.J.M. (2007, January 2\u20135). Parameter Estimation of a DC\/DC Buck converter using a continuous time model. Proceedings of the European Conference on Power Electronics and Applications, Aalborg, Denmark."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Buiatti, G.M., Amaral, A.M.R., and Cardoso, A.J.M. (2007, January 4\u20137). An Online Technique for Estimating the Parameters of Passive Components in Non-Isolated DC\/DC Converters. Proceedings of the IEEE International Symposium on Industrial Electronics, Vigo, Spain.","DOI":"10.1109\/ISIE.2007.4374665"},{"key":"ref_42","unstructured":"Buiatti, G.M., Amaral, A.M.R., and Cardoso, A.J.M. (October, January 30). An unified method for estimating the parameters of non-isolated DC\/DC converters using continuous time models. Proceedings of the 29th International Telecommunications Energy Conference, Rome, Italy."},{"key":"ref_43","unstructured":"Peng, Y., and Wang, H. (October, January 29). Application of Digital Twin Concept in Condition Monitoring for DC-DC Converter. Proceedings of the IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Soliman, H., Wang, H., Gadalla, B., and Blaabjerg, F. (2015, January 11\u201313). Condition monitoring for DC-link capacitors based on artificial neural network algorithm. Proceedings of the IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG), Riga, Latvia.","DOI":"10.1109\/PowerEng.2015.7266382"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Soliman, H., Abdelsalam, I., Wang, H., and Blaabjerg, F. (2017, January 3\u20137). Artificial Neural Network based DC-link capacitance estimation in a diode-bridge front-end inverter system. Proceedings of the IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, Kaohsiung, Taiwan.","DOI":"10.1109\/IFEEC.2017.7992442"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Soliman, H., Davari, P., Wang, H., and Blaabjerg, F. (2017, January 1\u20135). Capacitance estimation algorithm based on DC-link voltage harmonics using artificial neural network in three-phase motor drive systems. Proceedings of the IEEE Energy Conversion Congress and Exposition (ECCE), Cincinnati, OH, USA.","DOI":"10.1109\/ECCE.2017.8096961"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kamel, T., Biletskiy, Y., and Chang, L. (2015, January 3\u20136). Capacitor aging detection for the DC filters in the power electronic converters using ANFIS algorithm. Proceedings of the IEEE 28th Canadian Conference on Electrical and Computer Engineering, Halifax, NS, Canada.","DOI":"10.1109\/CCECE.2015.7129353"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Apsari, D., and Lee, D. (2024, January 17\u201320). Capacitance and ESR Estimation of DC-link Capacitors in AC Machine Drives Based on Hybrid CNN-Attention Model. Proceedings of the IEEE 10th International Power Electronics and Motion Control Conference, Chengdu, China.","DOI":"10.1109\/IPEMC-ECCEAsia60879.2024.10567930"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Rajendran, S., Jena, D., Diaz, M., and Devi, V.S.K. (2022, January 24\u201328). Machine learning based condition monitoring of a DC-link capacitor in a Back-to-Back converter. Proceedings of the IEEE International Conference on Automation\/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA), Curic\u00f3, Chile.","DOI":"10.1109\/ICA-ACCA56767.2022.10006052"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12606","DOI":"10.1109\/TPEL.2021.3135873","article-title":"Condition Monitoring of DC-Link Capacitors Using Time\u2013Frequency Analysis and Machine Learning Classification of Conducted EMI","volume":"37","author":"McGrew","year":"2022","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., Laadjal, K., and ACardoso, J.M. (2023). Advanced Fault-Detection Technique for DC-Link Aluminum Electrolytic Capacitors Based on a Random Forest Classifier. Electronics, 12.","DOI":"10.3390\/electronics12122572"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Amaral, A.M.R., Laadjal, K., and Cardoso, A.J.M. (2024, January 10\u201312). Assessment of the Integrity of Aluminum Electrolytic Capacitors Using a Logistic Regression Model. Proceedings of the IEEE International Conference on Artificial Intelligence & Green Energy (ICAIGE), Yasmine Hamm, Tunisia.","DOI":"10.1109\/ICAIGE62696.2024.10776711"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"48792","DOI":"10.1109\/ACCESS.2025.3551127","article-title":"A Second-Order Sliding Mode Control Scheme with Fuzzy Logic-Based Online Sliding Surface Adjustment for Buck Converters","volume":"13","author":"Kareem","year":"2025","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1109\/TIA.2025.3529797","article-title":"Applications of Data-Driven Dynamic Modeling of Power Converters in Power Systems: An Overview","volume":"61","author":"Subedi","year":"2025","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"6234","DOI":"10.1109\/TIA.2019.2937856","article-title":"Dynamic Modeling and Analysis of Buck Converter Based Solar PV Charge Controller for Improved MPPT Performance","volume":"55","author":"Venkatramanan","year":"2019","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"18325","DOI":"10.1109\/ACCESS.2025.3533032","article-title":"Modeling and Control of a Three-Phase Interleaved Buck Converter as a Battery Charger","volume":"13","author":"Mammeri","year":"2025","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5763","DOI":"10.1109\/TPEL.2021.3131594","article-title":"A High-Light-Load-Efficiency Low-Ripple-Voltage PFM Buck Converter for IoT Applications","volume":"37","author":"Kim","year":"2022","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_58","first-page":"4458","article-title":"A 94% Peak Efficiency Dual Mode Buck Converter with Fully Integrated On-Time-Based Mode Control for Implantable Medical Devices","volume":"69","author":"Park","year":"2022","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"313","DOI":"10.3390\/signals3020020","article-title":"Using Python for the Simulation of a Closed-Loop PI Controller for a Buck Converter","volume":"3","author":"Amaral","year":"2022","journal-title":"Signals"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1109\/TDEI.2019.007721","article-title":"Modeling of power supplies for power modulators with LTspice","volume":"26","author":"Giesselmann","year":"2019","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jovi\u0107, A., Brki\u0107, K., and Bogunovi\u0107, N. (2015, January 25\u201329). A review of feature selection methods with applications, 2015. Proceedings of the 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.1109\/MIPRO.2015.7160458"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Nasir, I.M., Khan, M., Yasmin, M., Shah, J., Gabryel, M., Scherer, R., and Damasevicius, R. (2020). Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training. Sensors, 20.","DOI":"10.3390\/s20236793"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Njimbouom, S., Lee, K., Lee, H., and Kim, J. (2023). Predicting Site Energy Usage Intensity Using Machine Learning Models. Sensors, 23.","DOI":"10.3390\/s23010082"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wang, W., Lu, L., and Wei, W. (2022). A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors. Sensors, 22.","DOI":"10.3390\/s22197121"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Supurtulu, M., Hatipoglu, A., and Y\u0131lmaz, E. (2025). An Analytical Benchmark of Feature Selection Techniques for Industrial Fault Classification Leveraging Time-Domain Features. Appl. Sci., 15.","DOI":"10.3390\/app15031457"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/13\/6971\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:55:56Z","timestamp":1760032556000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/15\/13\/6971"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,20]]},"references-count":65,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["app15136971"],"URL":"https:\/\/doi.org\/10.3390\/app15136971","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,20]]}}}