Multi-stage cyber-physical fusion methods for supporting equipment’s digital twin applications

The cyber-physical data of equipment whole life cycle are interrelated and play an important role in decision-making for business activities. However, multi-stage cyber-physical data are numerous, heterogeneous, and massive; non-real-time information data and real-time physical data are challenging to correspond to each other because of the inconsistency in space-time and granularity. As a result, equipment multi-stage heterogeneous cyber-physical data are difficult to effectively integrate and apply. Therefore, multi-stage cyber-physical data fusion of equipment is a key problem to be solved in manufacturing. To the best of our knowledge, a practicable framework and method to effectively fuse and apply equipment multi-stage data are still missing. To overcome this gap, this study proposes a multidimensional and multilevel cyber-physical fusion method for equipment. First, a four-dimensional cyber-physical fusion framework based on time, data, model, and structure is put forward. Then, the four kinds of fusion, including data fusion, model fusion, knowledge fusion, and digital twin (DT) application fusion, are discussed detailedly. Besides, the five types of algorithms set to support the fusion process are given. Finally, equipment life cycle activity is regarded as a DT application process using dynamic association of data and models. Through cyber-physical fusion, the data of equipment multi- stages could be applied for different activities to solve problems by means of DT. As a primary verification of the feasibility of the proposed approach, a case study of metro vehicle performance evaluation has been carried out, and the results have well confirmed that the multi-stage cyber-physical fusion method is feasible.