风力发电机组叶片故障诊断  

Fault Diagnosis of the Wind Turbine Blades

作　　者：李大冰 吉荣廷[1,2] 冯文秀[1] 

机构地区：[1]内蒙古工业大学,内蒙古呼和浩特010050 [2]内蒙古铁路科研所,内蒙古呼和浩特010050

出　　处：《节能技术》2013年第6期534-536,共3页Energy Conservation Technology

摘　　要：风机叶片的裂纹和断裂是导致风机机组事故的重要因素之一,尽早诊断出风机叶片的故障部位与故障程度,对安全生产具有意义重大。本文将叶片振动信号作为研究对象,利用小波分解方法对其进行信号分解,并与时域和频域方法处理结果进行对比分析,得出诊断结论。仿真结果表明:小波分解方法可以更有效的获取故障特征信号,具有较高的故障诊断率。Many factors may lead to wind turbine unit accidents,of which wind turbine blade cracks and fracture an important one.Diagnosing the position and degree of the fant on the wind turbine blades is more significance for safety in production.This paper uses the blade vibration signal as the research object,and wavelet decomposition method as signal decomposition,then comparing the results of time domain with frequency domain to get Diagnostic conclusion.The simulation results show that the wavelet decomposition method can be more effective to get the fault characteristic signal,and it has high fault diagnostic rate.

关 键 词：风机叶片 小波分解 振动信号 时域 频域 故障诊断 

分 类 号：TM614[电气工程—电力系统及自动化] TP302[自动化与计算机技术—计算机系统结构]