Search

Digital twins for medium span bridges – pilot project bridge Schwindegg – part 2: Asset Administration Shell The term ′digital twin′ is becoming increasingly popular in German‐speaking countries for the digital management and condition monitoring of bridges. Although standardised developments for the use of digital twins have already been initiated in industry and medicine, the first prototypes are currently being implemented in the construction industry. The digital images are typically operated in online environments that are proprietary to the project and have limited reusability. The article investigates the integration of scaling into Building Information Modelling models, which is reaching its limits due to the real‐time capability. It places existing definitions of the digital twin of the industry in the context of the construction industry. The implementation of the Isenbridge in Schwindegg as an Industry 4.0 Asset Administration Shell (AAS) is presented, building on Part 1 of this contribution. We show that an industrial, data‐driven digital twin can also be implemented technically and semantically for engineering structures. The article also presents the future challenges for evaluation and control mechanisms still to be developed for the structure. We also show a possible visualisation. The goal is to develop a syntax for the AAS' communication. [ABSTRACT FROM AUTHOR]

Transformer is a key equipment for constructing power system, and its safe and stable operation is the basis of power system. In order to solve the problems of difficult identification and low detection accuracy of transformer winding at operating state, in this paper, a diagnosis method based on digital twinning technique for transformer winding failure is proposed. Firstly a digital twin transformer model with high simulation degree based on transformer entities was built, and then multiple physical field simulation were used to deduce the information change of oil tank surface vibration information of digital twin transformer under different working conditions, various winding failures. Here, the vibration signal on oil tank is effectively decomposed to extract the feature vector by independent component analysis method and wavelet packet transform, which in turn is based on neural network algorithm to learn and diagnose the failure signal of winding. In order to test the accuracy and feasibility of the method, the diagnostic test of oil tank vibration signal detection of health and fault transformer is carried out on an experimental prototype of transformer with 110 kV, and the diagnostic results show that this method can diagnose the problems occurred in transformer windings with high efficiency and accuracy. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]