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

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 metho...

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Bibliographic Details
Published in:Metrology and Measurement systems 2023-01, Vol.30 (4), p.773-790
Main Authors: Yi, Lingzhi, Long, Jiao, Wang, Yahui, Sun, Tao, Huang, Jianxiong, Huang, Yi
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Eigenvalues
Fault diagnosis
Feature extraction
Frequency domain analysis
golden jackal optimization
Impact damage
induction motor
Induction motors
Machine learning
Motor stators
Neural networks
Rotors
Time-frequency analysis
time-frequency domain feature extraction
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Summary: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 highprecision 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.
ISSN:2300-1941
2080-9050
2300-1941
DOI:10.24425/mms.2023.147949