Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors
Abstract
:1. Introduction
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2. Symptoms of Induction Motor Circuit Faults Visible in Axial Leakage Flux
3. Application of Neural Networks in the Detection of Induction Motor Damages
3.1. General Remarks
3.2. Multilayer Feedforward Network
3.3. Self-Organizing Kohonen Map
3.4. Recurrent Hopfield Network
4. Experimental Verification of the Tested Fault Detectors
4.1. Description of the Experimental Set-Up and Conducted Tests
- 0–10 shorted turns of one stator phase,
- 0–3 damaged rotor cage bars.
4.2. Result of the Fault Detection Using MLP Networks
4.3. Result of the Fault Detection Using Kohonen Netoworks
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- the reduced stator fault detection efficiency is caused by the fact, that zones for Nsh = 1 and Nbb = 1 are close each other at high load,
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- the decrease in the effectiveness of stator damage grading results from too many overlapping neurons in smaller SOM; in the case of a (20 × 20) map active neurons are more “spread out” on the map,
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- reduced efficiency for the detection and classification of rotor damages by SOM (10 × 10) results from small distances between the zones for Nbb = 3 and Nbb = 2.
4.4. Result of the Fault Detection Using RHN
4.5. Comparison of the Used Neural Network Structures
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- the MLP network requires well-chosen signals in terms of changes due to damage (preferably linear changes);
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- the SOM was characterized by the ability to recognize patterns with a small amount of data. For the SOM, you do not have to choose the ‘ideal’ symptoms, because the individual categories will be classified anyway, while the input signals should not be associated with each other (correlations << −1);
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- in the case of RHN, a large size of the input vector with information resistant to interference is required.
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- in the case of MLP, the largest database of training patterns was required due to mixed damage tested;
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- in the case of SOM it was important that each category had a similar number of learning data;
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- RHN reached about 100% effectiveness for 200 training samples, therefore there was no need to enlarge the database.
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Name of the Parameter | Symbol | Units | |
---|---|---|---|
Power | PN | 1500 | [W] |
Torque | TN | 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 | Nrb | 26 | [-] |
Number of stator turns in each phase | Nst | 312 | [-] |
Shorted Turns | Broken Bars | No Fault | ||
---|---|---|---|---|
Kohonen map (20 × 20) | Approximate effectiveness of faults detection | 93% | 95% | 95% |
Approximate effectiveness of fault level classification | 70% | 93% | 95% | |
Kohonen map (10 × 10) | Approximate effectiveness of faults detection | 90% | 92% | 95% |
Approximate effectiveness of fault level classification | 65% | 88% | 95% |
Evaluation Categories | Multilayer Perceptron Network | Self-organizing Kohonen Map | Recurrent Hopfield Network | |
---|---|---|---|---|
EXPERIMENTAL VERIFICATION | The effectiveness of early detection of electrical damages | HIGH | MEDIUM | LOW |
The effectiveness of the stator damages level assessment | HIGH | MEDIUM | MEDIUM | |
The effectiveness of the rotor damages level assessment | HIGH | HIGH | MEDIUM | |
The effectiveness of the mixed damages level assessment | HIGH | LOW | LOW | |
Resistance to interference of the diagnostic signal | MEDIUM | HIGH | LOW | |
Hardware implementations | EASY | EASY | DIFFICULT | |
Interpretation of the neural network response | EASY | DIFFICULT | EASY | |
LEARNING PROCESS | Required size of the learning vector | LARGE | SMALL | LARGE |
Selection of the components of input vector | DIFFICULT | EASY | DIFFICULT | |
Selection of the neural network structure | HARD | EASY | EASY | |
Selection of the neural network learning parameters | HARD | EASY | EASY | |
Learning process time | LONG | MEDIUM | SHORT |
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Skowron, M.; Wolkiewicz, M.; Orlowska-Kowalska, T.; Kowalski, C.T. Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors. Energies 2019, 12, 2392. https://doi.org/10.3390/en12122392
Skowron M, Wolkiewicz M, Orlowska-Kowalska T, Kowalski CT. Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors. Energies. 2019; 12(12):2392. https://doi.org/10.3390/en12122392
Chicago/Turabian StyleSkowron, Maciej, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, and Czeslaw T. Kowalski. 2019. "Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors" Energies 12, no. 12: 2392. https://doi.org/10.3390/en12122392