Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

• Autorzy: Yi, Lingzhi , Long, Jiao , Wang, Yahui , Sun, Tao , Huang, Jianxiong , Huang, Yi
• Języki publikacji: EN

Abstrakty

EN

Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve high-precision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value.

Słowa kluczowe

EN

induction motor; time-frequency domain feature extraction; Golden Jackal Optimization; fault diagnosis

• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2023
• Tom: Vol. 30, nr 4
• Strony: 773--790
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr., wzory

Twórcy

autor

Yi, Lingzhi

• College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, 411105, China,
• Hunan Engineering Research Center of Multi-Energy Cooperative Control Technology, Xiangtan, Hunan 411105, China

autor

Long, Jiao

• College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, 411105, China,

autor

Wang, Yahui

• College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, 411105, China,

autor

Sun, Tao

• State Grid Anhui Electric Power Ultra-High Voltage Company, Hefei, Anhui, 230000, China,

autor

Huang, Jianxiong

• College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, 411105, China,

autor

Huang, Yi

• College of Automation and Electronic Information, Xiangtan University, Xiangtan, Hunan, 411105, China

Bibliografia

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Uwagi

This work was supported by the National Natural Science Foundation of China (61572416), Hunan
Province Natural Science Zhuzhou United Foundation (2022JJ50132), and the Postgraduate Scientific Research Innovation Project of the Hunan Province (QL20210153).

Identyfikatory

DOI

10.24425/mms.2023.147949