Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Aug 18;25(16):5128.
doi: 10.3390/s25165128.

A Review of Artificial Intelligence Techniques in Fault Diagnosis of Electric Machines

Christos Zachariades  1 , Vigila Xavier  1
Affiliations
Review

A Review of Artificial Intelligence Techniques in Fault Diagnosis of Electric Machines

Christos Zachariades et al. Sensors (Basel). .

Abstract

Rotating electrical machines are critical assets in industrial systems, where unexpected failures can lead to costly downtime and safety risks. This review presents a comprehensive and up-to-date analysis of artificial intelligence (AI) techniques for fault diagnosis in electric machines. It categorizes and evaluates supervised, unsupervised, deep learning, and hybrid/ensemble approaches in terms of diagnostic accuracy, adaptability, and implementation complexity. A comparative analysis highlights the strengths and limitations of each method, while emerging trends such as explainable AI, self-supervised learning, and digital twin integration are discussed as enablers of next-generation diagnostic systems. To support practical deployment, the article proposes a modular implementation framework and offers actionable recommendations for practitioners. This work serves as both a reference and a guide for researchers and engineers aiming to develop scalable, interpretable, and robust AI-driven fault diagnosis solutions for rotating electrical machines.

Keywords: artificial intelligence; condition monitoring; fault diagnosis; machine learning; predictive maintenance; rotating electrical machines.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Wind turbine bearing fault diagnostic model using SVM for measuring feature weights proposed by [31].
Figure 2
Figure 2
The multitask parallel CNN with reinforced input (RI-MPCNN) model for wind turbine gearbox fault diagnosis proposed by [44].
Figure 3
Figure 3
Wind turbine fault diagnosis framework based on parameter-based transfer learning and convolutional autoencoder proposed by [54].
Figure 4
Figure 4
Framework for PMSM fault diagnosis using multi-sensor signal fusion and image feature extraction proposed by [62].
Figure 5
Figure 5
The hybrid LSTM model with GWO for detection of multiple bearing faults proposed by [69].
Figure 6
Figure 6
Conceptual depiction of the application of digital twins used for rotating machine fault diagnosis [77].
Figure 7
Figure 7
Modular framework for practical implementation of AI-based fault diagnosis of electric machines.
See this image and copyright information in PMC

References

    1. Waide P., Brunner C.U. Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems. International Energy Agency (IEA); Paris, France: 2011.
    1. ABB . Value of Reliability: ABB Survey Report 2023—Industry’s Perspective on Maintenance and Reliability. ABB; Zurich, Switzerland: 2023.
    1. Andrew A.M. Fault Diagnosis: Models, Artificial Intelligence, Applications. Kybernetes. 2005;34:742–743. doi: 10.1108/03684920510595427. - DOI
    1. Yang R., Zhong M. Machine Learning-Based Fault Diagnosis for Industrial Engineering Systems. 1st ed. CRC Press; Boca Raton, FL, USA: 2022.
    1. Wilhelm Y., Reimann P., Gauchel W., Mitschang B. Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models. Procedia CIRP. 2021;99:278–283. doi: 10.1016/j.procir.2021.03.041. - DOI

LinkOut - more resources