摘要
为进一步提高煤矿提升机故障诊断能力水平,针对煤矿提升机电机系统故障特征,以音频信号为故障诊断的关键要素,首先基于小波变换方法确定信号特征提取与处理方法,而后以残差网络与通道注意力机制相结合的故障诊断方法为基础,通过对残差模块、池化层等关键环节进行设计,建立一套基于智能技术的煤矿提升机故障诊断方法。从实验测试结果来看,该方法相较于传统的SVM、CNN等方法而言,在诊断准确率上具有一定优势,因此该方法相对更具实用价值。
To further enhance the fault diagnosis capability of coal mine hoists,this study focuses on the characteristic features of motor system failures.Audio signals are identified as the key diagnostic element.First,a signal feature extraction and processing method is established based on the wavelet transform approach.Subsequently,a fault diagnosis method combining residual networks with channel attention mechanisms is developed.By designing critical components such as residual modules and pooling layers,an intelligent technology-based fault diagnosis method for coal mine hoists is established.Experimental results demonstrate that this method exhibits superior diagnostic accuracy compared to traditional approaches such as SVM and CNN,thereby offering greater practical value.
作者
李鹏
Li Peng(Tang'an Coal Mine Branch,Shanxi Lanhua Technology Co.,Ltd.,Gaoping Shanxi 048400,China)
出处
《机械管理开发》
2025年第12期95-97,共3页
Mechanical Management and Development
关键词
智能技术
煤矿提升机
故障诊断
诊断方法
intelligent technology
coal mine hoist
fault diagnosis
diagnostic method
