Influence of sample attributes on generalization performance of machine learning models for windage alteration fault diagnosis of the mine ventilation system

Rapid diagnosis and mitigation of windage alteration faults (WAFs) in the mine ventilation system is of great significance to create a good working environment and ensure safe production. The complexity of the ventilation system state evolution in time and space, and the high dimensionality of the feature set characterizing the system state leads to an exceptionally complex fault diagnosis. Therefore, this paper proposes to build a WAFs diagnosis model based on support vector machine (SVM). Meanwhile, the generalization performance of the model is significantly affected by the sample attributes. Therefore, the generalization errors of the model under the combination of four factors, i.e., sample dispersion, sample numbers, input features, and feature numbers, are further analyzed. The results show that the prediction effect of the model leads to a law that with the increase of the degree of sample dispersion, the number of samples, and the number of input features, it improves at the beginning and becomes stable in the end. Furthermore, the best feature for fault diagnosis is found as the combination of wind volume and wind pressure features. Finally, the correctness of the conclusion is verified by comparative experiments. This research provides useful guidance and valuable reference for how to build machine learning (ML) models for mine ventilation system fault diagnosis.