Abstract:
The article discusses the concept of using intelligent information and measuring systems (IIMS) built on the basis of digital twin technology to solve problems of predictive maintenance of industrial equipment. The architectural features, operating principles and key components of such systems are analyzed. The essence of a digital twin is revealed as a virtual copy of a physical object capable of reflecting its state and predicting behavior in real time. Particular attention is paid to the methods of collecting, processing and analyzing data, as well as the use of machine learning algorithms to build accurate predictive models. The article presents the main performance metrics and quality indicators used to evaluate models for predicting failures and the remaining life of equipment. Practical examples and industry cases of successful implementation of IIMS based on digital twins in such areas as mechanical engineering, energy and transport are considered. As a practical implementation, the concept of a hardware and software complex for monitoring and collecting statistical data on technological processes is proposed. The article demonstrates that the integration of digital twins into information and measuring systems is a promising direction for increasing the reliability, efficiency and economic feasibility of industrial equipment operation due to the transition from reactive and planned preventive maintenance strategies to a proactive, predictive approach.
Keywords:intelligent information and measuring system, digital twin, predictive maintenance, industrial equipment, machine learning, condition monitoring, failure prediction, residual resource, Industry 4.0, hardware and software complex.
