摘要
为了提高汽轮机故障诊断的精确性,文章运用转子振动实验台来模拟汽轮机转子的振动信号,对运行中的三种故障振动信号进行采集,然后运用局部特征尺度分解方法对汽轮机振动信号时间序列进行特征提取,组成特征向量。利用极限学习机作为故障诊断分类器,结果表明,局部特征尺度分解特征提取和极限学习机的诊断模型能够准确地对汽轮机故障进行诊断,具有很高的实际应用意义。
In order to improve the accuracy of fault diagnosis of steam turbine, this paper was used rotor vibration test bench to simulate the vibration signals of the steam turbine rotor, the operation of three kinds of fault vibration signal acquisition, and then using local characteristic scale decomposition approach to feature extraction steam turbine vibration signal time series of feature vector. Extreme learning machine was taken as a classifier of fault diagnosis. The results showed that the local characteristics of the scale decomposition of feature extraction and extreme learning machine diagnosis model can accurately the fault diagnosis for steam turbine, which had high practical application significance.
出处
《科技创新与生产力》
2014年第6期109-112,共4页
Sci-tech Innovation and Productivity
