Abstract: To eliminate the inaccurate fault predictions caused by the imbalanced samples in the communication network fault prediction dataset, a communication network fault prediction method based on sample equalization and feature interaction is proposed. Wasserstein generative adversarial networks-gradient penalty (WGAN-GP) is used to generate new minority samples to solve the problem of sample imbalance in the alarm dataset. Then a memory based feature generation by convolutional neural network (M-FGCNN) model is proposed. The proposed model is based on multi-layer perceptron and convolutional neural networks to strengthen the interaction between features. Meanwhile, the expert experience in the alarm field is used to generate new alarm features based on factorization machine model. Further, a memory vector is introduced to the model's embedding matrix and the loss function is modified to improve the memorability of the proposed model. The experimental results based on the public unbalanced dataset verify that the WGAN-GP model can generate new data with better quality than the existing models. The experimental results also verify that the proposed M-FGCNN model can achieve better communication network fault prediction performance than other deep learning models.

Key words: fault prediction, deep learning, generative adversarial networks