Design Technology and AI-Based Decision Making Model for Digital Twin Engineering

• Published in: Future internet Vol. 14; no. 9; p. 248
• Main Author: Orlova, Ekaterina V.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-09-2022
• Subjects: Artificial intelligence; artificial intelligence methods; Big Data; Complex systems; Data mining; Decision making; Decision making models; Design; digital twin; Digital twins; Engineering; Fault detection; Fuzzy logic; Internet; Machine learning; Mathematical models; Mathematical programming; methods for modeling digital twins; Numerical analysis; Operations research; Preventive maintenance; Product development; Sensors; Simulation; Simulation models; Statistical analysis; system design of digital twins
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi14090248

This research considers the problem of digital twin engineering in organizational and technical systems. The theoretical and methodological basis is a fundamental scientific work in the field of digital twins engineering and applied models. We use methods of a system approach, statistical analysis, operational research and artificial intelligence. The study proposes a comprehensive technology (methodological approach) for digital twin design in order to accelerate its engineering. This technology consists of design steps, methods and models, and provides systems synthesis of digital twins for a complex system (object or process) operating under uncertainty and that is able to reconfigure in response to internal faults or environment changes and perform preventive maintenance. In the technology structure, we develop a simulation model using situational “what-if” analysis and based on fuzzy logic methods. We apply this technology to develop the digital twin prototype for a device at the creation life cycle stage in order to reduce the consequences of unpredicted and undesirable states. We study possible unforeseen problems and device faults during its further operation. The model identifies a situation as a combination of failure factors of the internal and external environment and provides an appropriate decision about actions with the device. The practical significance of the research is the developed decision support model, which is the basis for control systems to solve problems related to monitoring the current state of technical devices (instruments, equipment) and to support adequate decisions to eliminate their dysfunctions.