Dynamically updated digital twin for prognostics and health management: Application in permanent magnet synchronous motor

Abstract

Current research on Digital Twin (DT) based Prognostics and Health Management (PHM) focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management. However, there still exist gaps in the seamless integration of DT and PHM, as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism. When the product experiences long-period degradation under load spectrum, it is challenging to describe the dynamic evolution of the health status and degradation progression accurately. In addition, DT update algorithms are difficult to be integrated simultaneously by current methods. This paper proposes an innovative dual loop DT based PHM framework, in which the first loop establishes the basic dynamic DT with multi-filed coupling, and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism. The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating. Furthermore, the Independence Principle for the abnormal detection is proposed to refine the theory of DT. Events at the first loop focus on accurate modeling of multi-field coupling, while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend. The interaction and collaboration between different loop models are also discussed. Finally, the Permanent Magnet Synchronous Motor (PMSM) is used to verify the proposed method. The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.