Zhan Kai, Wang Cong, Zheng Xigui, Kong Chao, Li Guangming, Xin Wei, Liu Longhe
Shandong Succeed Mining Safety Engineering Co. Ltd, Jinan, China.
Chengdu University of Technology, Chengdu, China.
Sci Rep. 2025 Jan 2;15(1):542. doi: 10.1038/s41598-024-84130-8.
In coal mining operations, the stable operation of hydraulic supports is crucial for ensuring mine safety. However, the nonlinear, non-stationary characteristics and noise interference in hydraulic support pressure data pose significant challenges for anomaly detection and fault diagnosis. This study proposes an anomaly detection and failure identification method based on Gated Recurrent Unit Autoencoder (GRU-AE), aimed at achieving anomaly detection in hydraulic support pressure data and equipment failure early warning. Through in-depth analysis of data from two coal mines in China, we systematically evaluated the model's key parameters. The study revealed that window size had a limited impact on model performance, with a window length of 144 demonstrating optimal comprehensive performance in both anomaly detection and failure mode identification. The study also investigated the effectiveness of teacher forcing techniques. Although this technique can accelerate model convergence, it may lead to training instability and reduced generalization capability, requiring careful consideration in practical applications. Our proposed Recurrent Reconstruction Network model demonstrated excellent performance in complex coal mine hydraulic support data, effectively identifying anomalous regions and potential equipment failure characteristics while revealing potential deviations between model predictions and actual data, demonstrating its superior learning capability for periodic data patterns and equipment failure characteristics. Experimental results validated the effectiveness of the GRU-AE model in hydraulic support pressure anomaly detection and equipment fault diagnosis, providing an innovative technical solution for coal mine safety monitoring.
在煤矿开采作业中，液压支架的稳定运行对于确保矿井安全至关重要。然而，液压支架压力数据中的非线性、非平稳特性以及噪声干扰，给异常检测和故障诊断带来了重大挑战。本研究提出了一种基于门控循环单元自动编码器（GRU-AE）的异常检测与故障识别方法，旨在实现液压支架压力数据中的异常检测和设备故障预警。通过对中国两个煤矿的数据进行深入分析，我们系统地评估了模型的关键参数。研究发现，窗口大小对模型性能的影响有限，窗口长度为144在异常检测和故障模式识别方面均表现出最佳的综合性能。该研究还考察了教师强制技术的有效性。虽然这种技术可以加速模型收敛，但可能导致训练不稳定和泛化能力下降，在实际应用中需要谨慎考虑。我们提出的循环重构网络模型在复杂的煤矿液压支架数据中表现出优异的性能，有效地识别出异常区域和潜在的设备故障特征，同时揭示了模型预测与实际数据之间的潜在偏差，证明了其对周期性数据模式和设备故障特征的卓越学习能力。实验结果验证了GRU-AE模型在液压支架压力异常检测和设备故障诊断中的有效性，为煤矿安全监测提供了一种创新的技术解决方案。
