Digital Twin Implementation for Predictive Maintenance in Industrial Systems

Abstract

The combination of Digital Twin (DT) and predictive maintenance (PdM) is catching on and can truly transform today’s industry, mainly driven by Industry 4.0. A Digital Twin is a live, digital version of a real asset, system or process that is constantly changing with updated information from sensors and advanced models. Because of this integration, it is easier to monitor, detect any anomalies, diagnose issues and predict future problems, leading to fewer unexpected shutdowns, savings on service costs and a better run of operations and asset health. It reviews in detail the implementation of Digital Twins for predictive maintenance, combining different perspectives across manufacturing, energy, aerospace, automotive and process domains. Important architectural pieces investigated by the study are IoT-enabled data collection, simulation with different physics, AI/ML analysis, frameworks using edge and cloud computing and advanced visual interfaces. A taxonomy of DT-based PdM systems organized by maintenance type (such as condition-based and failure prediction), modeling (such as physics-based, data-driven, hybrid) and maturity is provided. Besides, the review points out how major players in the field implement these technologies and assesses their outcomes. While significant progress has been made, problems including different forms of data, high costs in computing, explaining models, selecting industry standards and cybersafety keep slowing the adoption of AI across large organizations. The authors examine these barriers and come up with strong solutions in the paper. Various research areas are suggested, including making federated digital twins, adding semantic consistency to various systems, applying XAI for understandable decisions and enabling real-time analysis at the edge for quick responses. The purpose of the review is to bring together industrial and academic work to make it easier for researchers and users to build, put in place and perfect intelligent predictive maintenance using digital twins, encouraging strong, data-based industries.