{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T14:46:29Z","timestamp":1783521989823,"version":"3.55.0"},"reference-count":87,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"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>High efficiency and safety are critical factors in ensuring the optimal performance and reliability of systems and equipment across various industries. Fault monitoring (FM) techniques play a pivotal role in this regard by continuously monitoring system performance and identifying the presence of faults or abnormalities. However, traditional FM methods face limitations in fully capturing the complex interactions within a system and providing real-time monitoring capabilities. To overcome these challenges, Digital Twin (DT) technology has emerged as a promising solution to enhance existing FM practices. By creating a virtual replica or digital copy of a physical equipment or system, DT offers the potential to revolutionize fault monitoring approaches. This paper aims to explore and discuss the diverse range of predictive methods utilized in DT and their implementations in FM across industries. Furthermore, it will showcase successful implementations of DT in FM across a wide array of industries, including manufacturing, energy, transportation, and healthcare. The utilization of DT in FM enables a comprehensive understanding of system behavior and performance by leveraging real-time data, advanced analytics, and machine learning algorithms. By integrating physical and virtual components, DT facilitates the monitoring and prediction of faults, providing valuable insights into the system\u2019s health and enabling proactive maintenance and decision making.<\/jats:p>","DOI":"10.3390\/s23167087","type":"journal-article","created":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T10:52:40Z","timestamp":1691664760000},"page":"7087","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities"],"prefix":"10.3390","volume":"23","author":[{"given":"Jherson","family":"Bofill","sequence":"first","affiliation":[{"name":"Research and Development Center, Philippine Coding Camp, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mideth","family":"Abisado","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technologies, National University, Manila 1008, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jocelyn","family":"Villaverde","sequence":"additional","affiliation":[{"name":"School of Electrical, Electronics and Computer Engineering, Map\u00faa University, Manila 1002, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2354-4409","authenticated-orcid":false,"given":"Gabriel Avelino","family":"Sampedro","sequence":"additional","affiliation":[{"name":"Faculty of Information and Communication Studies, University of the Philippines Open University, Laguna 4031, Philippines"},{"name":"Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, F., Cui, Y., Wu, F., and Zhang, R. 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