摘要
针对水轮发电机组振动故障原因复杂、故障特征参数多的特点,提出了改进的主成分分析模型。利用模糊理论中隶属度函数归一化初始数据矩阵中各不同量纲的数据,并将其标准化。选取全部的主成分,统一了主成分个数的选取标准,也减少了对原始变量信息丢失。引入熵效用值理论确定各主成分的权重,可以显示出主成分含有原始变量信息的大小,采用主成分的加权灰关联度作为综合评判模型,解决了对主成分容量和规律性要求严格的问题。最后,以某水轮发电机组振动故障诊断为例,计算结果表明,该方法可以用于解决具有不同物理量振动故障特征参数的故障诊断。
The paper describes an improved principal component analysis model for hydro-turbine generating unit characterized by complicated causes of vibration faults and many fault parameters. This model uses the membership in fuzzy theory for normalization and standardization of the numerical values in the initial value matrix and selects all the principal components to integrate the selection standard and reduce information loss. For the principal components, their weights are specified by the theory of entropy utility value to represent the amount of information dimension of primal variable contained within them, and to meet the strict requirements by their volume and regularity, grey relational degree is used to calculate the weights for a comprehensive evaluation. A case study of a hydro-turbine generating unit indicates that the method is useful for vibration fault diagnosis involved with fault parameters of different physical quantities.
出处
《水力发电学报》
EI
CSCD
北大核心
2014年第3期279-285,共7页
Journal of Hydroelectric Engineering
基金
高层次人才科研启动项目(40203)
关键词
水轮发电机组
振动故障诊断
主成分分析
灰关联
hydro-turbine generating unit
vibration fault diagnosis
principal component analysis
diagnosis
grey relational
