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<p>Currently, research on fault Prognostics and Health Management (PHM) based on Digital Twin mainly focuses on integrating real-time data from various sources to facilitate comprehensive product inspection and health management. However, existing DT research faces three main theoretical bottlenecks: the lack of dynamic evolution mechanisms in multi-physics coupled modeling, static models’ difficulty in adapting to the drift of equipment degradation characteristics, and health status assessment’s reliance on prior fault samples. To address these issues, this paper proposes a comprehensive lifecycle dynamic management method for the Tubular Permanent Magnet Synchronous Electric Drum (TPMSED), by constructing a Dual-service Lifecycle Management Digital Twin Model (DSL-DT) that achieves deep integration of physical entities and virtual spaces. Firstly, a multi-physics coupled dynamic model is established, integrating the nonlinear interactions of electromagnetic fields, temperature fields, and dynamic fields. This is achieved through a combination of finite element simulation and data-driven approaches, addressing the challenge of characterizing equipment performance degradation under complex operating conditions. Secondly, an innovative dual dynamic adjustment mechanism for compensator updates and parameter updates is designed, utilizing ridge regression algorithms and adaptive gradient algorithms to achieve online optimization of model parameters, effectively suppressing model mismatch during the degradation process. Lastly, a health index (HI)-based state assessment method is proposed, which triggers model updates by comparing characteristic deviations with thresholds, significantly enhancing the accuracy of Remaining Useful Life (RUL) predictions. Experimental validation on an intelligent conveying system development platform demonstrates that this method can accurately track the performance evolution of equipment throughout its lifecycle, providing a new theoretical paradigm and technical pathway for health management of complex electromechanical systems.</p>

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Research on full lifecycle health management of permanent magnet synchronous electric drum driven by digital twin with dynamic update

  • 期刊论文

  • 发表日期：

    2025年10月4日

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  • Wei Chen,
  • Weimin Wu,
  • Mei Zhang,
  • Huashun Li,
  • Qing Shi

摘要

Currently, research on fault Prognostics and Health Management (PHM) based on Digital Twin mainly focuses on integrating real-time data from various sources to facilitate comprehensive product inspection and health management. However, existing DT research faces three main theoretical bottlenecks: the lack of dynamic evolution mechanisms in multi-physics coupled modeling, static models’ difficulty in adapting to the drift of equipment degradation characteristics, and health status assessment’s reliance on prior fault samples. To address these issues, this paper proposes a comprehensive lifecycle dynamic management method for the Tubular Permanent Magnet Synchronous Electric Drum (TPMSED), by constructing a Dual-service Lifecycle Management Digital Twin Model (DSL-DT) that achieves deep integration of physical entities and virtual spaces. Firstly, a multi-physics coupled dynamic model is established, integrating the nonlinear interactions of electromagnetic fields, temperature fields, and dynamic fields. This is achieved through a combination of finite element simulation and data-driven approaches, addressing the challenge of characterizing equipment performance degradation under complex operating conditions. Secondly, an innovative dual dynamic adjustment mechanism for compensator updates and parameter updates is designed, utilizing ridge regression algorithms and adaptive gradient algorithms to achieve online optimization of model parameters, effectively suppressing model mismatch during the degradation process. Lastly, a health index (HI)-based state assessment method is proposed, which triggers model updates by comparing characteristic deviations with thresholds, significantly enhancing the accuracy of Remaining Useful Life (RUL) predictions. Experimental validation on an intelligent conveying system development platform demonstrates that this method can accurately track the performance evolution of equipment throughout its lifecycle, providing a new theoretical paradigm and technical pathway for health management of complex electromechanical systems.