煤炭运输设备故障自动识别系统研究

为了提高煤炭运输设备故障自动识别系统的智能化程度，利用传感器感知信息技术，对煤炭运输设备故障自动识别系统进行研究。采用传感器建立信号采集数学模型，感知设备运行信息，并消除运行信息中的干扰信号；采用抗干扰能力强的排列熵空间重构信号，提取煤炭运输设备故障信号特征；将设备故障信号特征作为SVM特征空间上的训练数据集，计算最优分类函数划分设备故障信号特征类别，根据划分的信号特征类别，自动识别输送机设备故障,确定煤炭运输设备技术参数和故障信号。测试结果表明，此次研究的故障自动识别方法提取的故障信号排列熵特征值具有明显的区分度，故障平均识别精度高达96.86%。

Research on automatic fault identification system of coal transportation equipment

In order to improve the intelligent degree of automatic fault identification of coal transportation equipment,automatic fault identification of coal transportation equipment based on sensor sensing information is studied.The sensor is used to establish the mathematical model of signal acquisition,sense the equipment operation information,and eliminate the interference signal in the operation information.The permutation entropy space with strong anti-interference ability is used to reconstruct the signal and extract the fault signal characteristics of coal transportation equipment.Taking the equipment fault signal features as the training data set in the SVM feature space,the optimal classification function is calculated to divide the equipment fault signal feature categories.According to the divided signal feature categories,the equipment faults of coal conveyor are automatically identified,the technical parameters and fault signals of coal transportation equipment are determined.
The test results show that the fault signal arrangement entropy eigenvalue extracted by the automatic fault identification method in this study has obvious discrimination,and the average fault identification accuracy is as high as 96.86%.