摘要
为了提高煤矿大型机电设备运行的安全性与可靠性,对设备进行准确、快速地故障诊断至关重要。基于此,提出了基于振动模态识别的煤矿大型机电设备故障诊断方法研究。首先,根据设备的结构特点和运行状况,考虑设备的运行工况,布置传感器测点,采集设备运行状态下的振动信号;其次,对多传感器信号进行融合处理,为后续故障诊断提供分析基础;再次,基于振动模态识别原理提取出能够反映设备状态的特征参数,包括振动信号的均值、最大值、均方根值特征;最后,建立煤矿大型机电设备故障征兆与故障因素的模糊集合,通过设备故障征兆模糊向量,确定故障程度与类型。试验结果表明,该方法故障诊断率可达到94%以上,表现出良好的准确性和可靠性。
In order to improve the safety and reliability of the operation of large-scale electromechanical equip-ment in coal mines,accurate and rapid fault diagnosis of the equipment is crucial.Based on this,fault diagnosis method for large-scale electromechanical equipment in coal mines based on vibration modal recognition is proposed.Firstly,based on the structural characteristics and operating conditions of the equipment,considering the operating conditions of the equipment,the points of sensor measurement are arranged to collect the vibration signals under the operating state of the equipment;Secondly,the multi-sensor signals are fused and processed to provide an analytical basis for the subsequent diagnosis of faults;thirdly,the characteristic parameters reflecting the state of the equipment are extracted based on the principle of vibration modal recognition,including the mean value,maximum value,root mean square value features of the vibration signals;Finally,the fuzzy set of failure signs and failure factors for large-scale electromechanical equipment in coal mines is established,and the degree and type of failure are determined by the fuzzy vectors of equipment failure signs.The experimental results show that the fault diagnosis rate of this method can reach more than 94%,demonstrating good accuracy and reliability.
出处
《山西焦煤科技》
CAS
2024年第2期29-32,共4页
Shanxi Coking Coal Science & Technology
关键词
煤矿大型机电设备
振动模态识别
故障诊断
Large-scale electromechanical equipment in coal mines
Vibration modal recognition
Fault diagnosis
