基于JADE算法的大型风力机主轴故障诊断研究  

Fault Diagnosis of Large-scale Wind Turbine Main Bearing Based on JADE

作　　者：刘欢[1] 王健 

机构地区：[1]沈阳化工大学信息工程学院,沈阳110142

出　　处：《控制工程》2017年第4期901-904,共4页Control Engineering of China

基　　金：辽宁省教育厅科学研究一般项目资助(L2014166);2016年度辽宁省博士科研启动基金指导计划项目资助(201601202)

摘　　要：应用盲源分离中的JADE算法解决噪声的消除、弱信号的特征提取和多故障源的分离等问题。对大型风力机主轴振动信号进行盲源分离得到准确的故障信号,并根据故障振动信号频谱诊断出故障。利用盲源分离技术可以有效地去除外来干扰,以提高故障诊断精度,解决实际中故障定位、早期故障诊断率低的难题。诊断结果表明,该方法可有效地分离振动信号,并通过信号处理结果的频率特征来识别轴承故障状态。从而清晰地看出主轴的振动特征频率,实现对风力机主轴的故障诊断。JADE algorithm for blind source separation is used for noise elimination, extraction of the weak signals, and the separation of multi-fault sources. Blind source separation is used in analyzing vibration signals of the main bearing and accurate fault signals has been got. According to the fault vibration signal spectrum, the malfunction is diagnosed. The blind source separation technology can be used to effectively remove foreign interferences, in order to improve the accuracy of fault diagnosis and solve the problem of fault location, and low rate of early fault diagnosis. The diagnosis results show that this method can effectively separate the vibration signals, and identify the fault status of the bearing through frequency characteristics of the signal processing results. Thus fault diagnosis of the main bearing can be successfully completed after clearly identifying the vibration frequency.

关 键 词：盲源分离 主轴 故障诊断 

分 类 号：TP206[自动化与计算机技术—检测技术与自动化装置]