TY  - JOUR
AU  - Fang, Zheng
AU  - Wang, Zongjin
AU  - Zhu, Yunhui
AU  - Chen, Hao
AU  - Zhao, Hongseng
AU  - Lu, Yongbo
AU  - Li, Binsheng
PY  - 2026
DA  - 2026/06/12
TI  - Current Status and Trend Analysis of Motor Fault Diagnosis
JO  - ICCK Transactions on Intelligent Cyber-Physical Systems
T2  - ICCK Transactions on Intelligent Cyber-Physical Systems
JF  - ICCK Transactions on Intelligent Cyber-Physical Systems
VL  - 1
IS  - 2
SP  - 66
EP  - 71
DO  - 10.62762/TICPS.2026.215789
UR  - https://www.icck.org/article/abs/TICPS.2026.215789
KW  - motor
KW  - fault diagnosis
KW  - current status
KW  - trend analysis
AB  - As the core power equipment and primary load in power systems, electric motors are involved throughout the entire power generation, transmission, substation, and distribution processes. Their operational reliability directly determines the safety and stability of the power grid. This paper provides a systematic analysis of the current status and future trends in motor fault diagnosis. Specifically, it summarizes the three mainstream approaches currently used in motor fault diagnosis: model-based methods, signal processing-based methods, and artificial intelligence-based methods, while highlighting the respective advantages and application limitations of each approach. The analysis reveals three prominent challenges in existing research: inefficient multi-source information fusion, difficulties in early weak fault detection, and insufficient stability in decision-making integration. Future research directions are identified, including mechanism-data dual-driven strategies, deep cross-dimensional feature fusion of current, vibration and temperature signals, denoising algorithms for faint fault features, and edge-based online diagnostic systems for full-life predictive health management. It clarifies that overcoming the limitations of single-domain analysis, establishing an efficient fusion framework, and enhancing adaptability to complex operating conditions are crucial for further improving diagnostic accuracy and ensuring reliable operation of motors and power supply systems.
SN  - 3071-2947
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Fang2026Current,
  author = {Zheng Fang and Zongjin Wang and Yunhui Zhu and Hao Chen and Hongseng Zhao and Yongbo Lu and Binsheng Li},
  title = {Current Status and Trend Analysis of Motor Fault Diagnosis},
  journal = {ICCK Transactions on Intelligent Cyber-Physical Systems},
  year = {2026},
  volume = {1},
  number = {2},
  pages = {66-71},
  doi = {10.62762/TICPS.2026.215789},
  url = {https://www.icck.org/article/abs/TICPS.2026.215789},
  abstract = {As the core power equipment and primary load in power systems, electric motors are involved throughout the entire power generation, transmission, substation, and distribution processes. Their operational reliability directly determines the safety and stability of the power grid. This paper provides a systematic analysis of the current status and future trends in motor fault diagnosis. Specifically, it summarizes the three mainstream approaches currently used in motor fault diagnosis: model-based methods, signal processing-based methods, and artificial intelligence-based methods, while highlighting the respective advantages and application limitations of each approach. The analysis reveals three prominent challenges in existing research: inefficient multi-source information fusion, difficulties in early weak fault detection, and insufficient stability in decision-making integration. Future research directions are identified, including mechanism-data dual-driven strategies, deep cross-dimensional feature fusion of current, vibration and temperature signals, denoising algorithms for faint fault features, and edge-based online diagnostic systems for full-life predictive health management. It clarifies that overcoming the limitations of single-domain analysis, establishing an efficient fusion framework, and enhancing adaptability to complex operating conditions are crucial for further improving diagnostic accuracy and ensuring reliable operation of motors and power supply systems.},
  keywords = {motor, fault diagnosis, current status, trend analysis},
  issn = {3071-2947},
  publisher = {Institute of Central Computation and Knowledge}
}