Permanent magnet motors (PMMs) offer advantages such as high efficiency

high power density

and precise control

making them widely used in large-scale national equipment like aerospace

transportation

offshore wind power

as well as small precision devices such as medical instruments. PMMs play an important role in driving China’s transformation from a “manufacturing giant” to a “manufacturing powerhouse”. As the operating environment of PMMs becomes increasingly complex

higher demands are placed on their reliability and safety for long-term continuous operation under complex conditions. As a result

fault diagnosis for PMMs has become a research hotspot in this field. Traditional fault diagnosis methods face challenges such as a lack of fault data

difficulty in establishing mechanistic models

and poor algorithm generalization

which limit their practical application in complex operating conditions. By constructing high-fidelity virtual models

digital twin technology can achieve real-time mapping between the physical and virtual models of the motor across various operating conditions

while also providing multidimensional fault data. This offers new opportunities for PMM fault diagnosis. This paper provides a systematic review of fault diagnosis techniques for PMMs based on digital twin technology. First

the common fault types of PMMs and the technical challenges faced by traditional diagnostic methods are discussed. Next

the basic principles and development overview of digital twin technology are presented

followed by an exploration of its advantages in PMM fault diagnosis. The paper then establishes an overall framework for PMM fault diagnosis based on digital twin technology and provides a detailed review of the key technologies involved. Finally

the paper summarizes the current issues in digital twin-based PMM fault diagnosis and offers perspectives on future research.