Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis Using Dropout Rate Improved Deep Sparse AutoEncoder
| Autor: | Kexing Xu, Botao Wang, Tingting Zheng, Baohui Ma |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network Computer science Stator 020208 electrical & electronic engineering 02 engineering and technology Autoencoder law.invention 020901 industrial engineering & automation Control theory law Softmax function 0202 electrical engineering electronic engineering information engineering Short circuit |
| Zdroj: | 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). |
| DOI: | 10.1109/iaeac.2018.8577680 |
| Popis: | Motor is one of the most frequently used machines in industry. Ensuring the reliability of motor and timely identifying the fault type of motor can guarantee the properly working and prevent the great loss. In this paper, a turn-to-turn short circuit of motor stator and unbalance power supply fault diagnosis system based on Deep Sparse Auto-Encoder and Softmax Classifier is proposed. The selection of neural network parameters and their influence are systematic given. A new method where dropout rates of Sparse Auto-Encoder are various in each layer is adopted to increase the stability and convergence speed of training process. Finally, the proposed system is applied to an 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 reaches 100%, 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: |
|