Turn-to-Turn Short Circuit of Motor Stator Fault Diagnosis Using Dropout Rate Improved Deep Sparse AutoEncoder

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.