期刊文献+

基于随机共振和多维度排列熵的水电机组振动故障诊断 被引量:14

Vibration fault diagnosis of hydropower unit by using stochastic resonance and multidimensional permutation entropy
原文传递
导出
摘要 针对强噪声背景下难以提取水电机组振动故障特征的问题,提出了一种基于随机共振(SR)去噪和多维度排列熵(MPE)提取振动信号特征向量的故障诊断方法。首先,采用随机共振对振动信号进行去噪,增强信号的信噪比;继而利用多维度排列熵提取去噪信号的特征向量,最后将其输入所建立的改进粒子群算法优化支持向量机(PSO-SVM)模型,实现故障的识别与诊断。仿真结果表明,该方法具有较高的诊断精度。 Aiming at the issue that the characteristics of hydropower unit vibration faults are difficult to extract under strong background noises, this paper presents a fault diagnosis method using the techniques of stochastic resonance(SR) denoising and multidimensional permutation entropy(MPE) for extraction of the characteristic vectors from vibration signals. This method first denoises a vibration signal using stochastic resonance to enhance its stochastic resonance, then uses MPE to extract its feature vectors. Taking the feature vectors as input, an improved particle swarm algorithm and support vector machine model is able to achieve identification and diagnosis of the signal faults. Our simulations show that the method enables the fault diagnosis of hydropower units with high accuracy.
出处 《水力发电学报》 EI CSCD 北大核心 2015年第12期123-130,共8页 Journal of Hydroelectric Engineering
基金 国家自然科学基金(51279161 51209172)
关键词 随机共振 多维度排列熵 支持向量机 故障诊断 水电机组 stochastic resonance multi-dimensional permutation entropy support vector machine faulty diagnosis hydropower unit
  • 相关文献

参考文献10

二级参考文献91

共引文献235

同被引文献151

引证文献14

二级引证文献97

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部