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Utilizing t-SNE with various distances for fault isolation in 7-phase electrical machines

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  • Zhang, Lu
  • Delpha, Claude
  • Diallo, Demba
  • Nguyen, Ngac-Ky

Abstract

Due to their structural resilience, electrical machines with more than three phases are increasingly used in high-power applications (transportation and energy production). However, monitoring them to increase reliability and improve system availability and safety is essential. This work exploits the properties in the frequency domain of electrical currents flowing in 7-phase electrical machines for fault type diagnosis and faulty phase isolation, especially in the case of incipient faults in the latter. These properties are derived from projecting the phase currents in the stationary frames. The fault features are the amplitudes of the fundamental component of the transformed currents (1F). Several distances are used for fault clustering in the t-distributed Stochastic Neighbour Embedding (t-SNE) framework. The clustering results are provided through graphical visualization and two metrics, the Silhouette Score (SS) and Davies–Bouldin Index (DBI), measuring intraclass and interclass distances. The results demonstrate that the proposed features effectively identify the fault type and faulty phase, even when the same incipient fault type affects different phases. Additionally, the Mahalanobis distance performs well in fault type isolation, and the various distance metrics exhibit consistently high performance in faulty phase isolation.

Suggested Citation

  • Zhang, Lu & Delpha, Claude & Diallo, Demba & Nguyen, Ngac-Ky, 2026. "Utilizing t-SNE with various distances for fault isolation in 7-phase electrical machines," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 240(C), pages 668-680.
    • Handle: RePEc:eee:matcom:v:240:y:2026:i:c:p:668-680
    DOI: 10.1016/j.matcom.2025.07.034
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    References listed on IDEAS

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    1. Cristina Morel & Ahmad Akrad, 2023. "Open-Circuit Fault Detection and Location in AC - DC - AC Converters Based on Entropy Analysis," Energies, MDPI, vol. 16(4), pages 1-20, February.
    2. Qing-Hua Zhang & Qin Hu & Guoxi Sun & Xiaosheng Si & Aisong Qin, 2013. "Concurrent Fault Diagnosis for Rotating Machinery Based on Vibration Sensors," International Journal of Distributed Sensor Networks, , vol. 9(4), pages 472675-4726, April.
    3. Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
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