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Advanced Diagnosis of Armature Winding Short-Circuit Faults in Variable Flux Reluctance Machines Using Information Fusion on Mechanical and Electrical Signals

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Abstract

Variable flux reluctance machines can be adopted in the field of built-in starter generator for aero-engine. It is critical to achieve reliable protection of the power generation system in harsh environments. The single signal makes it difficult to identify the initial fault accurately due to the little impact by a small-turn short-circuit on the electromagnetic field. Thus, this paper proposes a novel framework for multi-source information fusion fault diagnosis in VFRMs by extracted current signals manually and vibration signals automatically. Firstly, the armature winding short-circuit fault characteristics of the current and vibration signals in the VFRM are analyzed. Secondly, a multi-source fusion framework based on a kernel extreme learning machine combined with a multiscale convolutional neural network is presented according to the structural characteristics of the VFRM. Then, the Dempster-Shafer evidence theory is applied for achieving decision-level fusion. Finally, a four-phase 8/10-pole VFRM prototype with different AWSC faults is used to validate the proposed method. The results indicate that the fault diagnosis rate of the proposed method is 97.28%, which is 10.37% and 3.7% higher than vibration and current signals, respectively. It is more reliable and effective to identify different AWSC faults accurately in the early stages.

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Acknowledgements

This work was supported in part by the Chunhui Program by the Ministry of Education of China (HZKY20220084), Natural Science Foundation of Shanghai (21ZR1425400), and Shanghai Rising-Star Program (21QC1400200).

Funding

Shanghai Rising-Star Program, 21QC1400200, Yao Zhao, Natural Science Foundation of Shanghai, 21ZR1425400, Yao Zhao, Chunhui Program by the Ministry of Education of China, HZKY20220084, Yao Zhao

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Authors and Affiliations

  1. Dept. of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China

    Yao Zhao, Shunfu Lin, Fan Yang & Dongdong Li

  2. State Grid Shanghai Electric Power Company, Shanghai, China

    Zhibo Zhao

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  1. Yao Zhao
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  2. Zhibo Zhao
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  3. Shunfu Lin
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  4. Fan Yang
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  5. Dongdong Li
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Correspondence to Dongdong Li.

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Zhao, Y., Zhao, Z., Lin, S. et al. Advanced Diagnosis of Armature Winding Short-Circuit Faults in Variable Flux Reluctance Machines Using Information Fusion on Mechanical and Electrical Signals. J. Electr. Eng. Technol. 19, 2239–2250 (2024). https://doi.org/10.1007/s42835-023-01697-4

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