Deep Learning Approach for Hydraulic Support Anomaly Detection: Utilizing Convolutional Autoencoders and Dynamic Time Warping Technology

In recent years, technological advances and widespread monitoring equipment have led to significant time-series data in the mining industry, especially for hydraulic support pressure. This data is vital for miner safety and predicting pressure cycles. However, the harsh conditions of coal mines often degrade data quality, making anomaly detection essential. This study introduces an anomaly detection model based on Dynamic Time Warping (DTW) and convolutional autoencoders to identify anomalies in hydraulic support data. The model’s encoder consists of three convolutional layers and two pooling layers, while the decoder comprises five transposed convolutional layers, compressing sequence length to one-fourth of the original. By setting a 5-min sampling interval, with each sample containing 288 time steps and using a sliding window with a stride of 72, an optimal dataset is generated. Training results indicate that the model successfully detects anomaly points and subsequences, accurately learning and simulating normal operational patterns of hydraulic supports, achieving early anomaly detection. The model performs stably on both training and validation sets, with the reconstruction error (MSE) reduced to 0.001. For anomaly detection in the test set, we used the sum of the mean and standard deviation of the reconstruction error from the validation set as the detection threshold (0.0041). The results show that the mode of the number of anomaly points in the test samples is 8, with an average of approximately 10. Furthermore, this study analyzes the model's limitations under specific conditions and proposes improvements to enhance accuracy and robustness.