基于多特征时序标记Transformer的凿岩机故障分类与预测OA

In order to tackle the technical bottleneck of predicting jamming and empty drilling faults of rock drills in drill-and-blast tunnel construction,a method of fault classification and prediction of the rock drill based on multi-feature time-series labeling Transformer was proposed.By collecting the key high-frequency while-drilling parameters of the rock drill under various working conditions,and integrating the thresholds of these parameters in the faulty states,a labeled dataset of jamming and empty drillin was constructed.A multi-feature time-series labeling strategy was designed to convert raw data into sequences of embedding vectors with temporal relationships.Building upon this,a multi-head self-attention mechanism was employed to mine long-term dependencies among the multiple features.Combined with a feedforward neural network and a dynamic slicing optimization strategy,and enhanced by residual connections and layer normalization,a time-prospective Transformer model was constructed.This model ultimately achieved the dual functions of fault classification and prediction.The experimental results demonstrated that the proposed method achieved an accuracy of 93.233%in the classification and prediction of jamming and empty drilling faults of the rock drill,significantly outperforming comparative models such as CNN(convolutional neural network),LSTM(long short-term memory),CNN-LSTM,RNN(recurrent neural network),and iTransformer.Visualization results of features using t-SNE(t-distribution stochastic neighbour embedding)revealed superior intra-class clustering and inter-class separation characteristics for the proposed model.Furthermore,it exhibited the lowest training loss and an inference time of merely 0.014 6 s,meeting the real-time warning requirements.The research results provide a reliable technical approach for classifying and predicting the faults of rock drills under complex geological conditions.