Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors
Abstract
:1. Introduction
- bearings–~40%,
- stator faults–~38%,
- rotor faults–~10%,
- others–~12%.
2. Symmetrical Component Analysis of Electrical Winding Faults
- IsA, IsB, IsC—stator phase currents in steady state in phases A, B, C, respectively;
- I0, I1, I2—zero-, positive- and negative-sequence components of the stator current in steady state, respectively;
- .
- isA(t), isB(t), isC(t)—corresponding instantaneous current signals of the three phases,
- i0, i1, i2—instantaneous zero-, positive-, and negative-sequence symmetrical components of the stator current.
3. Design of the Neural Fault Classifier Based on the Kohonen Network
3.1. General Remarks
3.2. Self-Organizing Kohonen Network
4. Analysis of the Diagnostic Signals—Experimental Results
4.1. Short Description of the Experimental Setup
- 0 ÷ 5 shorted turns of one stator phase that constituted 1.6% of stator turns in phase A,
- 0 ÷ 2 damaged rotor cage bars that constituted 7.7% of rotor bars.
4.2. Results of Stator and Rotor Fault Extraction
5. Experimental Tests of SOM Classifiers of IM Winding Faults
5.1. Training Pattern Selection for Self-Organizing Kohonen Network
5.2. Results of Stator and Rotor Fault Classification
6. Conclusions
- -
- separate faults of stator winding (incipient short-turns; 1–5 turns),
- -
- separate faults of rotor winding (incipient rotor bar brakes; 1–2 bars),
- -
- simultaneous occurrence of stator and rotor incipient faults.
Author Contributions
Funding
Conflicts of Interest
References
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Name of the Parameter | Symbol | Units | |
---|---|---|---|
Power | PN | 1500 | [W] |
Torque | MN | 10.16 | [Nm] |
Speed | NN | 1410 | [r/min] |
Stator phase voltage | UsN | 230 | [V] |
Stator current | IsN | 3.5 | [A] |
Frequency | fsN | 50 | [Hz] |
Pole pairs number | pp | 2 | [-] |
Number of rotor bars | Nr | 26 | [-] |
Number of stator turns in each phase | Ns | 312 | [-] |
Training Vector Size | 104 | |
Test Vector Size | 150 | |
Number of Faults Categories | Shorted turns: 5 Broken bars: 3 | |
Neural Network Inputs | I2(fs), I2(5 s − 2fbb), I2(5fs − fbb) | |
Training Method | WTM (Winner Takes Most) | |
Neural Network Structure | 20 × 20 | 10 × 10 |
Number of Training Epochs | 700 | 600 |
Neural Network Topology | hexagonal | |
Neighbourhood Function | Gaussian | |
Neighbourhood Radius | 2 | |
Learning Rate | 0.8 |
Shorted Turns | Broken Cars | Mixed Faults | No Fault | |
---|---|---|---|---|
Approximate effectiveness of faults detection | 90 % | 93% | 72% | 95% |
Approximate effectiveness of faults classification | 74% | 93% | - | 95% |
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Skowron, M.; Wolkiewicz, M.; Orlowska-Kowalska, T.; Kowalski, C.T. Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors. Appl. Sci. 2019, 9, 616. https://doi.org/10.3390/app9040616
Skowron M, Wolkiewicz M, Orlowska-Kowalska T, Kowalski CT. Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors. Applied Sciences. 2019; 9(4):616. https://doi.org/10.3390/app9040616
Chicago/Turabian StyleSkowron, Maciej, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, and Czeslaw T. Kowalski. 2019. "Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors" Applied Sciences 9, no. 4: 616. https://doi.org/10.3390/app9040616
APA StyleSkowron, M., Wolkiewicz, M., Orlowska-Kowalska, T., & Kowalski, C. T. (2019). Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors. Applied Sciences, 9(4), 616. https://doi.org/10.3390/app9040616