摘要
及时准确发现风机主轴故障,对直驱式风电机组安全经济运行具有重要意义。针对这一问题,该文提出一种基于S能量熵的直驱式风电机组轴承故障诊断方法。该方法利用广义S变换分析直驱式风机轴承振动信号的时频特性,使信号的主要能量在时频域分布更加集中,提高了信号的时频集聚性,并通过能量熵对广义S矩阵进行特征提取,构成故障分析向量,结合VPMCD方法建立故障诊断模型,对故障分析向量进行分析诊断。该文故障诊断方法对信号进行广义S变换,对变换结果采用能量熵提取特征,通过基于VPMCD方法的故障诊断模型判断运行状态。将该文方法应用于风电机组轴承故障诊断中,实验结果证明了该方法的可行性和有效性。
Find the bearing failure timely and accurately, is of great significance to the safe and economic operation of direct drive type wind power generator. Therefore, a method named feature extraction based on S energy entropy was brought up in this paper. This method adopted the generalized S transform to adjust the time-frequency resolution of the vibration signals,that way the main energy of the vibration signals would gather more in the time-frequency domain, which improves the timefrequencyconcentration of the signals. The generalized S matrix was then reconstructed by utilizing the energy entropy toextract the feature and build the fault analysis vector. Eventually, through utilizing the variable predictive model based classdiscriminate (VPMCD) and constructing the fault identification model. The experimental results prove that the proposed method applied to the bearing fault diagnosis acquires a better correction rate.
作者
王子佳
Wang Zijia(Datang Environment Industry Group CO..Ltd,Beijing, 100097, China)
出处
《科技资讯》
2016年第29期36-39,共4页
Science & Technology Information
关键词
广义S变换
能量熵
VPMCD
风电机组
故障诊断
Generalized S transform
Energy entropy
VPMCD
Wind power generator
Fault diagnosis
