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The enhancement of the RT-FDD performance when the proposed approach is applied is proved with the Feature Importance, Confusion Matrix, and F1 Score analysis, reaching mean values of 85% and 100% in each case study. Finally, considering that faults are rare events, the sensitivity of the models to the number of faulty samples is analyzed.<\/jats:p>","DOI":"10.3390\/s22166138","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T03:15:27Z","timestamp":1660706127000},"page":"6138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["An Automated Machine Learning Approach for Real-Time Fault Detection and Diagnosis"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0392-3279","authenticated-orcid":false,"given":"Denis","family":"Leite","sequence":"first","affiliation":[{"name":"Mekatronik I.C. Automacao Ltda, R. Itapeva, 43a-Imbiribeira, Recife 51180-320, Brazil"},{"name":"Institute of Technological Innovation, University of Pernambuco, R. Benfica, 455-Madalena, Recife 50670-90, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6479-264X","authenticated-orcid":false,"suffix":"Jr.","given":"Aldonso","family":"Martins","sequence":"additional","affiliation":[{"name":"Institute of Technological Innovation, University of Pernambuco, R. Benfica, 455-Madalena, Recife 50670-90, Brazil"},{"name":"Stellantis, Rodovia BR 101 Norte, Km 13-15a, Goiana 32530-900, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5256-5279","authenticated-orcid":false,"given":"Diego","family":"Rativa","sequence":"additional","affiliation":[{"name":"Institute of Technological Innovation, University of Pernambuco, R. Benfica, 455-Madalena, Recife 50670-90, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1150-4904","authenticated-orcid":false,"given":"Joao F. L.","family":"De Oliveira","sequence":"additional","affiliation":[{"name":"Institute of Technological Innovation, University of Pernambuco, R. Benfica, 455-Madalena, Recife 50670-90, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4348-9291","authenticated-orcid":false,"given":"Alexandre M. A.","family":"Maciel","sequence":"additional","affiliation":[{"name":"Institute of Technological Innovation, University of Pernambuco, R. Benfica, 455-Madalena, Recife 50670-90, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Westbrink, F., Chadha, G.S., and Schwung, A. (2018, January 15\u201318). Integrated IPC for data-driven fault detection. Proceedings of the 2018 IEEE Industrial Cyber-Physical Systems, ICPS, Petersburg, Russia.","DOI":"10.1109\/ICPHYS.2018.8387672"},{"key":"ref_2","first-page":"1","article-title":"Machine Learning Approaches for Fault Detection in Semiconductor Manufacturing Process: A Critical Review of Recent Applications and Future Perspectives","volume":"36","author":"Arpitha","year":"2022","journal-title":"Chem. Biochem. Eng. Q."},{"key":"ref_3","unstructured":"Pouliezos, A., and Stavrakakis, G.S. (2013). Real Time Fault Monitoring of Industrial Processes, Springer Science & Business Media."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Cohen, J., Jiang, B., and Ni, J. (2020, January 5\u20138). Utilizing timed petri nets to guide data-driven fault diagnosis of PLC-timed event systems. Proceedings of the 2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS, Hachijo Island, Japan.","DOI":"10.1109\/SCISISIS50064.2020.9322691"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/TSMC.2016.2633392","article-title":"FBMTP: An Automated Fault and Behavioral Anomaly Detection and Isolation Tool for PLC-Controlled Manufacturing Systems","volume":"47","author":"Ghosh","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1049\/ip-gtd:19970799","article-title":"Fuzzy rule-based expert system for power system fault diagnosis","volume":"144","author":"Monsef","year":"1997","journal-title":"IEE-Proc.-Gener. Transm. Distrib."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.psep.2007.10.014","article-title":"Real-time fault diagnosis using knowledge-based expert system","volume":"86","author":"Nan","year":"2008","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104637","DOI":"10.1016\/j.conengprac.2020.104637","article-title":"Process fault diagnosis with model- and knowledge-based approaches: Advances and opportunities","volume":"105","author":"Li","year":"2020","journal-title":"Control. Eng. Pract."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ifacol.2019.10.007","article-title":"A Multi-Agent Approach Based onMachine-Learning for Fault Diagnosis","volume":"52","author":"Koujok","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3358","DOI":"10.1109\/TII.2020.3011069","article-title":"A Multiagent-Based Methodology for Known and Novel Faults Diagnosis in Industrial Processes","volume":"17","author":"Ragab","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1016\/j.neucom.2017.10.063","article-title":"A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: A case study on cryogenic propellant loading system","volume":"275","author":"Ren","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"021009","DOI":"10.1115\/1.4045663","article-title":"An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System","volume":"20","author":"Chiu","year":"2020","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Furukawa, Y., and Deng, M. (2020, January 10\u201313). Fault detection of tank-system using ChangeFinder and SVM. Proceedings of the International Conference on Advanced Mechatronic Systems, ICAMechS 2020, Hanoi, Vietnam.","DOI":"10.1109\/ICAMechS49982.2020.9310117"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Makridis, G., Kyriazis, D., and Plitsos, S. (2020, January 20\u201323). Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry. Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, Greece.","DOI":"10.1109\/ITSC45102.2020.9294450"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lee, J.S., and Chuang, C.C. (2009, January 3\u20135). Development of a Petri net-based fault diagnostic system for industrial processes. Proceedings of the IECON Proceedings (Industrial Electronics Conference), Porto, Portugal.","DOI":"10.1109\/IECON.2009.5414911"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.jmsy.2021.07.007","article-title":"Data science skills and domain knowledge requirements in the manufacturing industry: A gap analysis","volume":"60","author":"Li","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_17","first-page":"1","article-title":"AutoML to Date and Beyond: Challenges and Opportunities","volume":"54","author":"Santu","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106622","DOI":"10.1016\/j.knosys.2020.106622","article-title":"AutoML: A survey of the state-of-the-art","volume":"212","author":"He","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sader, S., Husti, I., and Dar\u00f3czi, M. (2020). Enhancing failure mode and effects analysis using auto machine learning: A case study of the agricultural machinery industry. Processes, 8.","DOI":"10.3390\/pr8020224"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1007\/s00170-022-08761-9","article-title":"Towards big industrial data mining through explainable automated machine learning","volume":"120","author":"Garouani","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Larocque-Villiers, J., Dumond, P., and Knox, D. (2021, January 28\u201329). Automating Predictive Maintenance Using State-Based Transfer Learning and Ensemble Methods. Proceedings of the IEEE International Symposium on Robotic and Sensors Environments, ROSE 2021, Virtual.","DOI":"10.1109\/ROSE52750.2021.9611768"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"116027","DOI":"10.1016\/j.eswa.2021.116027","article-title":"One-shot neural architecture search for fault diagnosis using vibration signals","volume":"190","author":"Li","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kefalas, M., Baratchi, M., Apostolidis, A., Van Den Herik, D., and Back, T. (2021, January 7\u20139). Automated Machine Learning for Remaining Useful Life Estimation of Aircraft Engines. Proceedings of the 2021 IEEE International Conference on Prognostics and Health Management, ICPHM 2021, Detroit, MI, USA.","DOI":"10.1109\/ICPHM51084.2021.9486549"},{"key":"ref_24","unstructured":"Ali, M. (2022, July 24). PyCaret: An Open Source, Low-Code Machine Learning Library in Python; PyCaret Version 2.3. Available online: https:\/\/www.marktechpost.com\/2020\/04\/18\/pycaret-an-open-source-low-code-machine-learning-library-in-python\/."},{"key":"ref_25","unstructured":"Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., and Grobler, J. (2013, January 23\u201327). API design for machine learning software: Experiences from the scikit-learn project. Proceedings of the ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Prague, Czech Republic."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"106773","DOI":"10.1016\/j.cie.2020.106773","article-title":"Machine learning applications in production lines: A systematic literature review","volume":"149","author":"Kang","year":"2020","journal-title":"Comput. Ind. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111872","DOI":"10.1016\/j.enbuild.2022.111872","article-title":"Energy & Buildings Real vs. simulated: Questions on the capability of simulated datasets on building fault detection for energy efficiency from a data-driven perspective","volume":"259","author":"Huang","year":"2022","journal-title":"Energy Build."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"19990","DOI":"10.1109\/ACCESS.2018.2890566","article-title":"A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning","volume":"7","author":"Xu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TMECH.2021.3065522","article-title":"Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks with Data Privacy","volume":"27","author":"Zhang","year":"2022","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7957","DOI":"10.1109\/TII.2021.3064377","article-title":"Universal Domain Adaptation in Fault Diagnostics with Hybrid Weighted Deep Adversarial Learning","volume":"17","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Informatics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106223","DOI":"10.1016\/j.petrol.2019.106223","article-title":"A realistic and public dataset with rare undesirable real events in oil wells","volume":"181","author":"Vargas","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_32","unstructured":"Lerner, U., Parr, R., and Koller, D. (August, January 30). Bayesian Fault Detection and Diagnosis in Dynamic Systems. Proceedings of the Aaai\/iaai, Austin, TX, USA."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/0098-1354(93)80018-I","article-title":"A plant-wide industrial process control problem","volume":"17","author":"Downs","year":"1993","journal-title":"Comput. Chem. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6138\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:10:49Z","timestamp":1760141449000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,17]]},"references-count":33,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166138"],"URL":"https:\/\/doi.org\/10.3390\/s22166138","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,17]]}}}