Song Qingjun, Sun Shirong, Song Qinghui, Wang Bingrui, Liu Zihao, Jiang Haiyan
College of Intelligent Equipment, Shandong University of Science and Technology, Taian, 271000, Shandong, China.
Sci Rep. 2025 Jan 2;15(1):101. doi: 10.1038/s41598-024-83604-z.
Coal-gangue recognition technology plays an important role in the intelligent realization of integrated working faces and coal quality improvement. However, the existing methods are easily affected by high dust, noise, and other disturbances, resulting in unstable recognition results that make it difficult to meet the needs of industrial applications. To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals. It combines traditional filtering methods and the idea of multi-scale learning, which can expand the breadth and depth of the feature learning process. the breadth and depth of the feature learning process. Moreover, to strengthen the expression of key features, a feature weighting method based on the attention mechanism is combined to give adaptive weights to different features. Finally, the experimental platform of a tail beam of coal-gangue impact hydraulic support is built, and several comparative experiments are carried out. The comprehensive comparison experiments show that the method shows strong adaptability, robustness, and noise resistance under various complex noise environments, and is suitable for complex practical industrial sites.
煤矸石识别技术在综采工作面智能化实现及煤炭质量提升方面发挥着重要作用。然而，现有方法容易受到高粉尘、噪声等干扰的影响，导致识别结果不稳定，难以满足工业应用需求。为实现噪声环境下煤矸石的准确识别，本文提出一种基于端到端多尺度特征融合卷积神经网络（MCNN-BILSTM）的矸石识别方法，该方法能够自动学习并融合振动信号多个信号分量中的互补信息。它结合了传统滤波方法和多尺度学习思想，可扩展特征学习过程的广度和深度。此外，为强化关键特征的表达，结合基于注意力机制的特征加权方法，为不同特征赋予自适应权重。最后，搭建了煤矸石冲击液压支架尾梁实验平台，并进行了多项对比实验。综合对比实验表明，该方法在各种复杂噪声环境下均表现出较强的适应性、鲁棒性和抗噪声能力，适用于复杂的实际工业现场。
