Intelligent fault diagnosis of mine ventilation system for imbalanced data sets

Abstract

It is of great significance to determine the location of fault branch timely and accurately to ensure the reliability and safety of mine ventilation system. To solve the problem that the traditional machine learning model has the poor diagnostic ability and generalization ability due to the imbalance of sample data in mine ventilation system under actual working conditions, a WGAN-div-RF fault diagnosis model is proposed. Taking a simple ventilation network as an example, the fault data sets with the imbalance ratios of 2∶1, 5∶1, 10∶1, 20∶1 are constructed, and the impact of imbalanced samples on the ventilation system fault diagnosis is analyzed indepth. The Wasserstein divergence for GANs (WGAN-div) is built, and the residual blocks are added innovatively to improve the quality of the generated data and expand the original sample set. Combined with the RF model, the fault diagnosis of ventilation system is realized. Taking the ventilation system of the Dongshan Coal Mine as the experimental object, the comparative experiments are carried out respectively with different data enhancement models, different classification models, and different data generation rates. The effectiveness of the model is evaluated with various indexes and t-SNE visualization. The results show that the data generated by the WGAN-div model with residual blocks has a good similarity to the real data. Compared with GAN, WGAN, and WGAN-gp, the WGAN-div model is superior. After applying the WGAN-div model for data augmentation, the performance of the machine learning classification model is significantly improved. When the expanded data set is balanced, compared with other integrated models and the commonly used SVM model for mine ventilation system fault diagnosis, the RF model is superior in Re, Pr, Gmean and F1 indexes.

Keywords

• mine ventilation system
• fault diagnosis
• imbalanced data
• generate adversarial network
• random forest