[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122423-en":3,"doc-seo-122423-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122423,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",6,"Technology","Machine Learning-Based Condition Monitoring with Novel Event Detection and Incremental Learning for Industrial Faults and Cyberattacks","The study proposes an integrated condition-monitoring framework for industrial processes that unifies fault diagnosis and cybersecurity. It combines an optimized computational-intelligence learning model with a novelty-detection mechanism to flag anomalous conditions absent from training data for expert review. Offline preparation includes labeling, normalization, and hyperparameter tuning. Online operation enables real-time monitoring and incremental learning to incorporate new events. The approach uses multilayer perceptron, local outlier factor, and differential evolution, validated on the two-tank benchmark with 99% detection accuracy.","Article  \nMachine Learning-Based Condition Monitoring with Novel Event Detection and Incremental Learning for Industrial Faultsand Cyberattacks  \nAdrián Rodríguez-Ramos 1,†, Pedro J. Rivera Torres 2,3, *,†, Antônio J. Silva Neto 1,†  \nand Orestes Llanes-Santiago 4,5,†  \nAcademic Editor: Zhibin Lin  \nReceived: 28 July 2025  \nRevised: 3 September 2025  \nAccepted: 15 September 2025  \nPublished: 18 September 2025  \nCitation: Rodríguez-Ramos, A.; Rivera Torres, P.J.; Silva Neto, A.J.; Llanes-Santiago, O. Machine Learning-Based Condition Monitoring with Novel Event Detection and Incremental Learning for Industrial Faults and Cyberattacks. Processes 2025, 13, 2984. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/pr13092984](10.3390/pr13092984)  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \n1 Laboratório LEMA-LEMEC, Instituto Politécnico, Universidade do Estado do Rio de Janeiro, Nova Friburgo 28625-570, RJ, Brazil; [arradrian@gmail.com](arradrian@gmail.com) (A.R.-R.); [ajsneto@iprj.uerj.br](ajsneto@iprj.uerj.br) (A.J.S.N.)  \n2 Departamento de Informática y Automática, Universidad de Salamanca, 37008 Salamanca, Spain  \n3 St. Edmund’s College, University of Cambridge, Cambridge CB3 0HE, UK  \n4 Programa de Pós-Graduação em Modelagem Computacional, Instituto Politécnico, Universidade do Estado do Rio de Janeiro, Nova Friburgo 28625-570, RJ, Brazil; [oresteslls@gmail.com](oresteslls@gmail.com)  \n5 Departamento de Automática y Computación, Universidad Tecnológica de la Habana José Antonio Echeverría, CUJAE, La Habana 10900, Cuba  \n* Correspondence: pedro.rivera@usal.es † These authors contributed equally to this work.  \nAbstract  \nThis study presents an integrated condition-monitoring approach for industrial processes. The proposed approach conveniently combines a computational intelligence-based mechanism to guarantee the resilience of the proposed scheme against unknown anomalies anda machine learning model with optimized parameters capable of unified detection and pinpointing of faults and cyberattacks in industrial plants. During the offline phase, process data are labeled, normalized, and used to train the machine learning model with hyperparameter tuned by using an optimization tool. In the online phase, the system performs real-time monitoring enhanced with a novelty mechanism to detect anomalous conditions not present in the training data, which are flagged for expert analysis and incorporated into the system through incremental learning. The implementation of the proposed strategy uses computational intelligence tools consisting of a multilayer perceptron neural network, local outlier factor, and differential evolution. The proposed framework was validated using the two-tank process benchmark, demonstrating superior detection accuracy of 99% and robustness compared to other machine learning algorithms. These results highlight the potential of combining fault diagnosis and cybersecurity in a unified architecture, thereby contributing to resilient and intelligent systems in the context of Industry 4.0/5.0 .  \nKeywords: Industry 4.0; condition monitoring; unknown events; machine learning tools  \n1. Introduction  \nThe fourth industrial revolution signifies a revolutionary shift in manufacturing and corporate environments, fueled by the convergence of cutting-edge innovations such asthe Industrial Internet of Things (IIoT), Cloud and Edge Computing, Big Data, Artificial Intelligence (AI), and Robotics [1] . Such advancements allow industries to enhance interconnectivity, automate workflows, and digitize operations, transforming legacy facilities into intelligent and interconnected Cyber-Physical Systems (CPS) [2,3] . While this f","cbCaivW86V8948Et","https://ap.wps.com/l/cbCaivW86V8948Et","pdf",2007406,1,22,"English","en",105,"# Introduction\n## Industrial revolutions and cyber-physical systems\n## Condition monitoring for faults and cybersecurity risks\n## Challenges and existing approaches","[{\"question\":\"What does the proposed framework combine to monitor industrial processes?\",\"answer\":\"It combines a computational-intelligence-based resilience mechanism with a machine learning model that performs unified detection and pinpointing of faults and cyberattacks.\"},{\"question\":\"How does the system handle anomalous events not seen during training?\",\"answer\":\"A novelty mechanism detects anomalous conditions not present in training data, flags them for expert analysis, and then incorporates them via incremental learning.\"},{\"question\":\"Which methods and tools are used in the implementation and how was it validated?\",\"answer\":\"It uses a multilayer perceptron neural network, local outlier factor, and differential evolution, validated on the two-tank process benchmark with 99% detection accuracy and robustness versus other approaches.\"}]","Machine Learning-Based Condition Monitoring with Novel Event Detection and Incremental Learning for Industrial Faults and Cyberattacks | 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does the proposed framework combine to monitor industrial processes?","Question",{"text":75,"@type":76},"It combines a computational-intelligence-based resilience mechanism with a machine learning model that performs unified detection and pinpointing of faults and cyberattacks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the system handle anomalous events not seen during training?",{"text":80,"@type":76},"A novelty mechanism detects anomalous conditions not present in training data, flags them for expert analysis, and then incorporates them via incremental learning.",{"name":82,"@type":73,"acceptedAnswer":83},"Which methods and tools are used in the implementation and how was it validated?",{"text":84,"@type":76},"It uses a multilayer perceptron neural network, local outlier factor, and differential evolution, validated on the two-tank process benchmark with 99% detection accuracy and robustness versus other 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