基于音频识别的采煤机滚筒载荷识别方法

针对现有采煤机滚筒载荷识别方法相关算法实施难度大、工程实现方式复杂、应用难度高等问题,通过分析采煤机工作时音频信号的特征,提出一种基于音频识别的采煤机滚筒载荷识别方法。为确保每个分析周期内的音频信号具有同一运行标准下的负载工况,将截割电流与牵引速度作为变量引入到动态能量计算中,采用动态能量归一化算法(DENA)对采煤机原始音频信号进行归一化处理;将归一化后的信号与标准工况库中的信号进行对比分析,通过最大相异系数判断两者之间的差异性,从而确定滚筒载荷特征,实现滚筒载荷识别判断。试验结果表明:DENA可有效抑制音频信号中的噪声能量,提升音频信号中关键特征值的分辨率,采煤机在截割煤、岩时的音频信号特征参数界限明显,未出现交叉混叠现象;在理想情况下,即最大相异系数小于0.189时,总的煤岩界面识别率可达到78.6%。

Shearer drum load identification method based on audio recognition

In order to solve the problems of the existing shearer drum load identification methods,such as difficult implementation of related algorithms,complex engineering implementation mode and high application difficulty,through analyzing the characteristics of the audio signal during shearer operation,a shearer drum load identification method based on audio recognition is proposed.In order to ensure that the audio signal in each analysis period has the same load condition under the same operation standard,the cutting current and the traction speed are introduced into the dynamic energy calculation as variables,and the dynamic energy normalization algorithm(DENA)is adopted to normalize the original audio signal of the shearer.The normalized signal is compared and analyzed with the signal in the standard operation condition library,and the difference between the two is judged by the maximum dissimilarity coefficient,so as to determine the characteristics of the drum load and realize the identification and judgment of the drum load.The test results show that DENA can effectively suppress the noise energy in the audio signal and improve the resolution of the key characteristic values in the audio signal.The boundary of the characteristic parameters of the audio signal is obvious when the shearer cuts coal and rock,and there is no cross aliasing phenomenon.Under ideal conditions,that is,when the maximum dissimilarity coefficient is less than 0.189,the total coal-rock interface recognition rate can reach 78.6%.