摘要
针对风机运行过程中存在的非线性非平稳特征,提出一种相空间与最小二乘支持向量机(LS-SVM)相结合的风机状态预测方法。首先利用相空间重构方法将一维的时间序列拓展到高维相空间中,还原出风机运行的动力学行为;然后将高维空间中的拓扑结构输入到最小二乘支持向量机中,利用其非线性拟合的优势,最终实现风机状态的趋势预测。利用该方法与BP神经网络方法分别对工业现场的风机振动信号进行对比分析,最大预测误差从7.22%下降到3.75%,说明在相同样本数的条件下,新方法能够更准确地预测风机的振动状态,可为维修决策提供更可靠的数据支持。
According to the characteristics of nonlinearity and nonstationarity which occur in the working process of fans a trend prediction method is proposed based on the combination of phase space and least squares support vector machine （LS-SVM） for condition of fans. Firstly, a time series is extended into the high dimensional phase space using the phase space reconstruction method. This high dimensional phase space can restore the dynamic behavior of condition of fans; then the topology of the high dimensional phase space is input to the LS-SVM. Due to the LS-SVM＇s advantage of nonlinear fitting, the condition of fans is thus predicted. The vibration signal of fans in industrial field is analyzed. The results show that, compared with BP neural net-work method, the maximum prediction error using the proposed method drops from 7. 22% to 3.75%. As our method can predict the condition of fans more accurately under the same sample size, it can provide more reliable data support for the maintenance decision.
出处
《中国科技论文》
CAS
北大核心
2013年第8期743-746,共4页
China Sciencepaper
基金
高等学校博士学科点专项科研基金资助项目(20090006120007)
国家自然科学基金资助项目(51004013)
关键词
风机
故障诊断
相空间方法
最小二乘逼近
支持向量机
fan
failure diagnosis
phase space method
least squares approximations
support vector machine
