DefinePK

Abstract

Digital twin technology has also gifted the field of predictive maintenance with substantial technological developments in industrial systems that provide real-time monitoring, identification of faults, and maintenance of assets in the most desirable forms. Digital twins combine sensor data, machine learning, and simulation to prevent failure through the prediction of failure and reduced downtimes, and reduce maintenance costs by modeling physical equipment into a virtual one. The paper focuses on discussing the architecture of the predictive maintenance solution based on digital twin, with a particular focus on the IoT-based sensors as the source of data information, cloud computing as an enabler of scalable analytics, and AI-powered algorithms to detect anomalies. An example of a case study of the manufacturing industry illustrates that digital twins can enhance equipment reliability by 30 percent and cut unplanned outages by 25 percent. Future problems like data security, model accuracy, and computational requirements are also critically examined, and the possible solutions to overcome these are suggested, including edge computing and federated learning. The intended use of Industry 4.0 technologies, such as augmented reality (AR) to guide the technicians, and blockchain, etc., in the context of the digital twins is also noted in the study. Findings show that the industries that embrace the concept of digital twins gain 20 percent more in operational efficiency and a 15 percent decrease in the cost of maintenance. The future research directions mentioned at the end of the paper include the possibility of incorporating quantum computing to get faster simulations and applying the digital twins to achieve sustainable energy management. Digital twin technology can close the digital divide between the physical and digital world to provide a solid platform of predictive maintenance, opening up to a smarter, more resilient industrial ecosystem.