解决煤矿瓦斯和煤尘爆炸监测手段落后的问题,迟报,和低泄漏率,提出了一种基于GoogLeNet的煤矿瓦斯煤尘爆炸声音识别方法。在煤矿的重点监控区域安装采矿拾音器以收集工作设备和环境的声音后,用连续小波对采集到的声音进行分析,得到其尺度系数图。然后将其导入GoogLeNet,获得煤矿瓦斯和煤尘爆炸的识别模型。通过连续小波分析获得测试声音,获得比例系数图,引入到完成的训练识别模型中,以获得声音信号类,并通过实验验证。首先,连续小波分析从声音信号中提取的尺度系数图表明,瓦斯爆炸声和煤尘爆炸声的小波系数图的主客观指标相似度较高,但是这些声音和其他煤矿声音之间的区别更明显,有助于有效区分气体和煤尘爆炸声与其他声音。其次,可以获得GoogLeNet参数的实验结果。当dropout参数为0.5,初始学习率为0.001时,GoogLeNet建立的模型识别效果最优。根据选择的参数,训练损失,测试损失,训练识别率,测试模型的识别率均符合预期。最后,实验识别结果表明,该方法的识别率为97.38%,召回率为86.1%,并且对于测试数据与训练数据的比率为9:1的情况,准确率为100%,提出的GoogLeNet的整体识别效果明显优于vgg和Alexnet,能有效解决煤矿瓦斯和煤尘爆炸声音的欠采样问题,满足煤矿瓦斯和粉尘爆炸智能识别的需要。
To solve the problems of backward means of coal mine gas and coal dust explosion monitoring, late reporting, and low leakage rate, a sound recognition method of coal mine gas and coal dust explosion based on
GoogLeNet was proposed. After installing mining pickups in key monitoring areas of coal mines to collect the sounds of the working equipment and the environment, the collected sound was analyzed by continuous wavelet to obtain its scale coefficient map. This was then imported into
GoogLeNet to obtain the recognition model of coal mine gas and coal dust explosions. The test sound was obtained by continuous wavelet analysis to obtain the scale coefficient map, brought into the completed training recognition model to obtain the sound signal class, and verified by experiment. Firstly, the scale coefficient map extracted from the sound signal by continuous wavelet analysis showed that the similarity between the subjective and objective indicators of the wavelet coefficient maps of the gas explosion sound and coal dust explosion sound was higher, but the difference between these and the rest of the coal mine sounds was clearer, helping to effectively distinguish gas and coal dust explosion sounds from other sounds. Secondly, the experimental results of
GoogLeNet parameters can be obtained. When the dropout parameter is 0.5 and the initial learning rate is 0.001, the recognition effect of the model established by
GoogLeNet was optimal. According to the selected parameters, the training loss, testing loss, training recognition rate, and testing recognition rate of the model are all in line with expectations. Finally, the experimental recognition results show that the recognition rate of the proposed method is 97.38%, the recall rate is 86.1%, and the accuracy rate is 100% for the case of a 9:1 ratio of test data to training data, and the overall recognition effect of the proposed
GoogLeNet is significantly better than that of vgg and Alexnet, which can effectively solve the problem of under-sampling of coal mine gas and coal dust explosion sounds and can meet the need for the intelligent recognition of coal mine gas and dust explosions.