An automatic rotor bar fault diagnosis using fuzzy logic and DWT-energy for backstepping control driven induction motor in low-speed operation

The contribution of rotor bar fault diagnosis and classification in low-speed induction motor drives under varying load torques has been a topic of limited discussion in the literature. Within this respect, the present paper will be discussing this condition. To ensure control performance and invest...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2023-08, Vol.27 (15), p.10411-10426
Main Authors: Ameid, Tarek, Ammar, Abdelkarim, Talhaoui, Hicham, Azzoug, Younes, Chebaani, Mohamed
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Artificial Intelligence
Classification
Computational Intelligence
Control
Control theory
Discrete Wavelet Transform
Eigenvalues
Engineering
Engineering Sciences
Failure
Fault detection
Fault diagnosis
Fourier transforms
Fuzzy logic
Fuzzy Systems and Their Mathematics
Induction motors
Low speed
Magnetic fields
Mathematical analysis
Mathematical Logic and Foundations
Mechatronics
Robotics
Rotors
Signal processing
Speed regulators
Wavelet transforms
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Summary:The contribution of rotor bar fault diagnosis and classification in low-speed induction motor drives under varying load torques has been a topic of limited discussion in the literature. Within this respect, the present paper will be discussing this condition. To ensure control performance and investigate the diagnosis process in low-speed operations, the backstepping algorithm is utilized to handle uncertainties. The discrete wavelet transform is employed to detect faults and decompose signals at various levels, while the fuzzy logic algorithm is applied to classify the fault severity. This study's novelty lies in evaluating fault severity for no/low-load conditions by using the energy approximation obtained from the discrete wavelet transforms of the speed regulator's output signal. This approximation is then used as input for the fuzzy fault classification algorithm. The control algorithm and fault diagnosis are validated experimentally using MATLAB/Simulink with a real-time interface based on dSpace 1104 implementation. The results obtained from this procedure demonstrate successful fault detection and severity classification using both the discrete wavelet approximation and its energy eigenvalue.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08443-y