Sun Lin, Lin Lisen, Yao Xulong, Zhang Yanbo, Tao Zhigang, Ling Peng
Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China.
College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China.
Sensors (Basel). 2023 Oct 17;23(20):8513. doi: 10.3390/s23208513.
The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.
岩石变形与破裂过程中产生的声发射信号特征蕴含着丰富的岩石内部损伤信息。利用声发射监测技术可以分析和识别岩石破坏的前兆信息。目前，在声发射监测与岩石破裂灾害预警领域，尚无针对灾害前兆特征信号的实时识别方法。通过分析传统声发射的特征参数来寻找作为灾害前兆的信号容易丢失信息，且分析大多基于事后分析，导致对灾害前兆特征的实时识别能力较差，在工程领域的应用水平较低。基于此，本文将岩石破裂的声发射信号视为岩石破裂产生的一种语音信号，借鉴语音识别的思路并结合频谱分析（STFT）和梅尔频率分析来实现岩石破裂声发射的特征提取。在深度学习方面，基于VGG16卷积神经网络和AlexNet卷积神经网络，构建了六个岩石破裂及关键声发射信号的智能实时识别模型，并对传统VGG16的网络结构和损失函数进行了优化。实验结果表明，这六个深度学习模型能够实现关键信号的实时智能识别，其中梅尔与改进后的VGG16相结合，取得了最佳性能，准确率为87.68%，召回率为81.05%。然后，通过比较多组信号识别模型，验证了本文提出的Mel+VGG-FL具有较高的识别准确率和一定的识别效率，能够更准确地对岩石破裂过程中的关键声发射信号进行智能实时识别，可为相关研究及岩石破裂前兆特征的实时智能识别提供新的思路和方法。
