Development of a digital twin system in a special transformer production workshop

To address the weak real-time control, poor decision-making ability, nonintuitive process monitoring, and unclear operation situation problems in a special transformer production workshop, a new digital twin system architecture is discussed for the workshop, focusing on digital twin modeling, knowledge information fusion, operation reliability analysis, and visual representation. The modeling and expression of the digital twin were based on the “Five dimensions–Four perspectives” engine, where the “Five dimensions” refer to physical entities, virtual objects, twin data, connection mapping, and services and the “Four perspectives” refer to geometric, physical, behavioral, and rule models, with high-performance computing power and immersive visualization technology. This paper describes the method of 3D modeling, data mapping, computing power development, and visual expression for the digital twin. A deep fusion method between knowledge and digital twin was studied based on the principle of tree growth. This model includes two key parts. First, a design of the index system having a hierarchical structure based on the principle of tree growth was proposed. Indices were divided into four levels—workshop, production line, station, and equipment levels—based on the workshop scale, vertically constituting the main branches of the index tree model. From the perspective of the life cycle of production activities, indices were divided into planning parameters, process parameters, work order parameters, quality parameters, equipment parameters, and so on. Thus, a complete index system of the digital twin workshop was established. Furthermore, the photosynthesis and the transport of organic products in the tree were simulated by formally describing the deep fusion mechanism of knowledge and digital twin in transformer workshops, and diverse intelligent computing units were built to mine knowledge and identify information from disorganized workshop operation big data. Finally, a multilevel and complex model was constructed to integrate the knowledge and digital twin of a special transformer workshop based on the computation model, and the real-time monitoring and control of the workshop were realized. An operational reliability analysis model of the digital twin system for special transformer workshops based on composite matter element information entropy was also proposed, which combines the analytical hierarchy process and the correlation entropy method. A composite matter element model was systematically built based on the historical operational data of the transformer workshop, which can be used for the real-time monitoring of the operational reliability and the evolution of any trends in the operational parameters. Thus, a prototype digital twin system was developed, and its rationality and effectiveness were verified using the special transformer workshop as the application case. The research results have an outstanding reference for the construction of digital twin systems and the intelligent management of transformer workshops.