试论小波分析技术支持下的煤矿机电设备故障检测关键技术应用

Application of Key Technology of Fault Detection of Mechanical and Electrical Equipment in Coal Mine Supported by Wavelet Analysis Technology

煤矿机电设备工作环境恶劣,需加强设备状态监测,确保及时发现轴承等核心部件的早期故障。针对设备早期故障特征信号幅值小的情况,提出小波分析检测技术,根据故障信号突变特点实现故障精准识别。通过掌握故障特征提取、联动检测等故障检测关键技术,结合采煤机轴承故障信号特征对技术应用方法进行探索,最终确定故障检测准确率可达92%,成功解决了煤矿机电设备故障在线诊断难题。

The working environment of mechanical and electrical equipment in coal mines is harsh,so it is necessary to strengthen the condition monitoring of equipment to ensure the timely detection of early failures of core components such as bearings.In view of the small amplitude of the early fault characteristic signal of the equipment,the wavelet analysis detection technology is proposed to achieve accurate fault identification according to the sudden change characteristics of the fault signal.By mastering the key fault detection technologies such as fault feature extraction and linkage detection,and exploring the technical application methods combined with the fault signal characteristics of shearer bearings,the fault detection accuracy rate is finally determined to reach 92%,which successfully solves the problem of online fault diagnosis of coal mine electromechanical equipment.