Exploring Digital Twin-Based Fault Monitoring: Challenges and Opportunities

Abstract

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’s health and enabling proactive maintenance and decision making.

Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)

“…Out of the reviewed works, it has been observed from the literature that few attempts have been made to showcase the benefits of distributed DT implementation for predictive maintenance as a manufacturing industry solution. As such, this work will go further to present a DT framework that attempts to cover these gaps [ 21 , 23 ], summarized in Figure 1 .…”

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

“…The establishment of a digital operation and maintenance system reduces the impact of unpredictable factors, such as the aging and wear of equipment and structural deviations. This enables more accurate and efficient operation and maintenance compared to traditional manual experience-based systems [189].…”