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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 53723 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Mokhtari, Hossein
- Abstract:
- In this thesis, a model is presented to detect and monitor the rotor bar's condition of large motors. This proposed model uses two diagnostic methods MCSA and ZCT, to extract the fault components. The input of the proposed model is only the motor current at two levels of 80% and 100% of the nominal motor load, which by using the two methods MCSA and ZCT and making changes in how to use them can be the disadvantages of other methods such as incorrect detection of rotor bars in Large motors with variable load, the harmonical stator voltage (or the presence of the drives) and asymmetric conditions. The extracted components are classified using two learnable algorithms, the k-NN algorithm and neural network, instead of experimental and human resources constraints. Using these two classification algorithms is high accuracy and the ability to implement in conventional processors. The proposed model has been trained and tested using currents of real large motor data and 96/87% accuracy using the k-NN algorithm to detect the number of faulty rotor bars and monitor their status and accuracy of 91/67% network monitor the online status of the rotor bars. On the other hand, the proposed model has been implemented in the industrial environment (Firoozkooh Cement Factory) and has been able to correctly determine the rotor bars of 4 large suspicious motors announced by the factory technicians
- Keywords:
- Condition Monitoring ; Classification ; Neural Network ; Motor Current Signature Analysis (MCSA) ; K-Nearest Neighbor Method ; Large Induction Motor ; Faulty Rotor Bar Detection
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