基于数字孪生的电机故障诊断研究与展望
Research and Prospects of Digital Twin-Based Fault Diagnosis of Electric Machines.
作者信息
Hu Jiaqi, Xiao Han, Ye Zhihao, Luo Ningzhao, Zhou Minhao
机构信息
School of Electrical Engineering, Naval University of Engineering, Wuhan 430033, China.
出版信息
Sensors (Basel). 2025 Apr 21;25(8):2625. doi: 10.3390/s25082625.
This paper focuses on the application of digital twins in the field of electric motor fault diagnosis. Firstly, it explains the origin, concept, key technology and application areas of digital twins, compares and analyzes the advantages and disadvantages of digital twin technology and traditional methods in the application of electric motor fault diagnosis, discusses in depth the key technology of digital twins in electric motor fault diagnosis, including data acquisition and processing, digital modeling, data analysis and mining, visualization technology, etc., and enumerates digital twin application examples in the fields of induction motors, permanent magnet synchronous motors, wind turbines and other motor fields. A concept of multi-phase synchronous generator fault diagnosis based on digital twins is given, and challenges and future development directions are discussed.
本文重点关注数字孪生在电动机故障诊断领域的应用。首先,阐述了数字孪生的起源、概念、关键技术及应用领域,比较分析了数字孪生技术与传统方法在电动机故障诊断应用中的优缺点,深入探讨了数字孪生在电动机故障诊断中的关键技术,包括数据采集与处理、数字建模、数据分析与挖掘、可视化技术等,并列举了数字孪生在感应电动机、永磁同步电动机、风力涡轮机等电机领域的应用实例。给出了基于数字孪生的多相同步发电机故障诊断概念,并讨论了面临的挑战和未来的发展方向。