Bofill, J.; Abisado, M.; Villaverde, J.; Sampedro, G.A. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors2023, 23, 7087.
Bofill, J.; Abisado, M.; Villaverde, J.; Sampedro, G.A. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors 2023, 23, 7087.
Bofill, J.; Abisado, M.; Villaverde, J.; Sampedro, G.A. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors2023, 23, 7087.
Bofill, J.; Abisado, M.; Villaverde, J.; Sampedro, G.A. Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities. Sensors 2023, 23, 7087.
Abstract
High efficiency and safety are critical factors in ensuring 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’s health and enabling proactive maintenance and decision-making.
Keywords
3D printing; nozzle clogging; machine learning; smart monitoring
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
