| Summary: | The machine learning algorithm predicts unknown data by learning known data. Most of the existing fault diagnosis methods of ventilation system focus on a machine learning algorithm, which can not guarantee the selected algorithm to be optimal. In order to solve this problem, eight machine learning algorithms are compared, and three algorithms, support vector machine ( SVM), random forest and neural network, are selected to study the fault diagnosis of ventilation network. According to the actual layout of the mine ventilation system, a ventilation network pipeline model is constructed according to the criteria of geometric similarity, motion similarity and dynamic similarity. A ventilation network consisting of pipeline network branches and pipeline network nodes is obtained, and air volume data is obtained through experiments, and the data is preprocessed by a standardized method. Through cross-validation and grid search, the parameters of ventilation network fault diagnosis model based on SVM, random forest and neural network are optimized. The results of experiment and field test show that the accuracy of ventilation network fault diagnosis model based on SVM, random forest and neural network are 0.89, 0.88 and 0.95 respectively on the test set of experimental platform, and 0.86, 0.90 and 0.96 respectively on the test set of coal mine field. The neural network model has the best fault diagnosis effect. 120 sets of fresh air volume data collected in coal mine field are input into neural network model for prediction, and the fault diagnosis accuracy rate reaches 0.98, which verifies the feasibility and accuracy of the ventilation network fault diagnosis model based on neural network.
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