Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis in Continuous State Based on Deep Auto-Encoder
| Autor: | Chuanwen Shen, Tingting Zheng, Botao Wang, Kexing Xu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science Stator law Control theory 020208 electrical & electronic engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 02 engineering and technology Short circuit Autoencoder law.invention |
| Zdroj: | 2018 IEEE International Power Electronics and Application Conference and Exposition (PEAC). |
| DOI: | 10.1109/peac.2018.8590283 |
| Popis: | The motor is one of the most commonly used equipment in the industry. It is necessary to ensure the reliability of the motor, and identify the type of motor fault in time to ensure the normal operation of the motor and reduce the loss. In this paper, a turn-to-turn short circuit of motor stator and unbalance power supply fault diagnosis system based on Deep Auto-Encoder and Soft-max Classifier is proposed. The influence of neural network parameters on the training process and the choice of parameters are given. The proposed fault diagnosis system can map the motor state to a 2-dimension vector, corresponding to different area of a plane to identify different fault type. Finally, the proposed system is verified by experiment on a motor in laboratory. The conclusion shows the ability to identify the fault type of motor at the continuous state that the accuracy is above 99.5%, when only the data from motor at discrete state point are used in training, which makes the system extensible and promising. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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