Xu Jing, Wang Zhongbin, Tan Chao, Si Lei, Liu Xinhua
School of Mechatronic Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China.
School of Information and Electrical Engineering, China University of Mining & Technology, No. 1 Daxue Road, Xuzhou 221116, China.
Sensors (Basel). 2015 Oct 30;15(11):27721-37. doi: 10.3390/s151127721.
In order to guarantee the stable operation of shearers and promote construction of an automatic coal mining working face, an online cutting pattern recognition method with high accuracy and speed based on Improved Ensemble Empirical Mode Decomposition (IEEMD) and Probabilistic Neural Network (PNN) is proposed. An industrial microphone is installed on the shearer and the cutting sound is collected as the recognition criterion to overcome the disadvantages of giant size, contact measurement and low identification rate of traditional detectors. To avoid end-point effects and get rid of undesirable intrinsic mode function (IMF) components in the initial signal, IEEMD is conducted on the sound. The end-point continuation based on the practical storage data is performed first to overcome the end-point effect. Next the average correlation coefficient, which is calculated by the correlation of the first IMF with others, is introduced to select essential IMFs. Then the energy and standard deviation of the reminder IMFs are extracted as features and PNN is applied to classify the cutting patterns. Finally, a simulation example, with an accuracy of 92.67%, and an industrial application prove the efficiency and correctness of the proposed method.
为保证采煤机的稳定运行，推动自动化采煤工作面的建设，提出一种基于改进的总体经验模态分解（IEEMD）和概率神经网络（PNN）的高精度、快速在线截割模式识别方法。在采煤机上安装工业麦克风，采集截割声音作为识别依据，以克服传统探测器体积大、接触式测量和识别率低的缺点。为避免端点效应并去除初始信号中不需要的固有模态函数（IMF）分量，对声音信号进行IEEMD处理。首先基于实际存储数据进行端点延拓以克服端点效应。接着引入由第一个IMF与其他IMF的相关性计算得到的平均相关系数来选择基本IMF。然后提取剩余IMF的能量和标准差作为特征，并应用PNN对截割模式进行分类。最后，一个准确率为92.67%的仿真示例和工业应用证明了该方法的有效性和正确性。
