基于小波分析和改进神经网络的采煤机截割部传动系统故障诊断

Fault Diagnosis of Shearer Cutting Part Transmission System Based on Wavelet Analysis and Improved Neural Network

为了解决当前采煤机截割部传动系统故障诊断方法存在耗时长、误差大等问题,提出了基于小波分析和改进神经网络的采煤机截割部传动系统故障诊断方法。首先采集采煤机截割部传动系统故障数据,并采用小波分析对数据进行预处理,消除噪声的不利影响;然后提取故障特征并输入神经网络进行训练,采用粒子群算法优化神经网络的参数,从而建立采煤机截割部传动系统故障诊断模型。仿真实验结果表明,该方法提升了采煤机截割部传动系统故障诊断精度,大幅度减少了故障诊断时间,而且增强了抗噪声干扰能力。

In order to solve the problems of long time-consuming and large error existing in the current fault diagnosis method of the transmission system of the cutting part of shearer, a fault diagnosis method of the transmission system of the cutting part of shearer based on wavelet analysis and improved neural network was proposed. Firstly, the fault data of the transmission system of the cutting part of the shearer were collected, and the wavelet analysis was used to preprocess the data to eliminate the adverse effects of noise. Then the fault characteristics were extracted and input into the neural network for training,and the particle swarm optimization algorithm was used to optimize the parameters of the neural network,so as to establish the fault diagnosis model of the transmission system of the cutting part of the shearer.The simulation experimental results show that this method improves the fault diagnosis accuracy of shearer cutting part transmission system, greatly reduces the fault diagnosis time, and enhances the ability of anti noise interference.