摘要
为对风机齿轮箱状态进行准确评估,提出一种基于核主成分(KPCA)与最小二乘支持向量机(LS-SVM)结合的齿轮箱数据融合故障预测模型。鉴于SCADA系统中数据信息质量的不确定性和冗余性,该模型首先对监测数据预处理(4分位法剔除异常数据等),对齿轮箱特征因素进行相关性分析,利用该预测方法对齿轮箱典型状态特征(振动、温度特征等)进行预测,利用统计过程控制原理(SPC)分析残差,以实现齿轮箱异常状态的预测。最后,以齿轮箱油温预测为例,验证了该模型的准确性和有效性。
For an accurate evaluation of wind turbine gearbox condition,a kind of based on the kernel principal component analysis(KPCA)and least squares support vector machines(LS-SVM)combined with gearbox data fusion fault prediction model is put forward,in terms of the uncertainty of SCADA system data in the information quality and redundancy,firstly the model for monitoring data preprocessing(quartile method,a method to eliminate abnormal data),the correlation analysis was carried out on the gear box characteristic factors,by using the forecast method of gearbox typical state characteristics(vibration,temperature characteristics,etc.).By using the theory of statistical process control(SPC)analysis of the residual,the abnormal condition of gear box is predicted.Finally,the model for the accuracy and effectiveness are verified by taking the oil temperature prediction of gearbox as an example.
作者
王炜超
袁逸萍
孙文磊
赵琴
樊盼盼
贾依达尔·热孜别克
Wang Weichao;Yuan Yiping;Sun Wenlei;Zhao Qin;Fan Panpan;Jiayidaer·Rezibieke(College of Mechanical Engineering,Xinjiang University,Urumqi 830047,China)
出处
《机械科学与技术》
CSCD
北大核心
2020年第2期201-206,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
国家自然科学基金项目(51365054,51565055)资助.
关键词
数据融合
核主成分
最小二乘支持向量机
SCADA数据
统计过程控制原理
data fusion
kernel principal component
least squares support vector machines
SCADA data
statistical process control principle
作者简介
王炜超(1994-),硕士研究生,研究方向为风机数据处理及故障预测,973877238@qq.com;通信作者:袁逸萍(1973-),教授,博士生导师,yipingyuan@163.com
