【摘要】 提出了一种改进粒子滤波和小波包分析相结合的汽轮机振动故障诊断方法。针对传统粒子滤波的样本退化问题,在重采样阶段提出了一种权值排序和优胜劣汰的改进粒子滤波算法。采用小波包分析的方法进行特征提取,利用SVM得到故障诊断结果。由结果可知,降噪信号的故障识别率明显高于原始信号的故障识别率。无论哪种信号,采用小波包分析提取特征向量进行故障诊断的识别率要高于采用FFT分析得到特征向量进行故障诊断的识别率,证明了本文提出方法的优越性。更多还原

【Abstract】 A fault diagnosis method of improved particle filter and wavelet packet analysis was proposed in the application of turbine vibration. There was a sample degradation problem in the re-sampling stage of traditional particle filter. And a re-sampling algorithm which was a weight sorting and the survival of the fittest to obtain the improved particle filter was studied. The signal was filtered by the improved particle filter. Then wavelet packet analysis was used to extract the features from the noise reduction signal. Finally the fault diagnosis results were obtained by using SVM. It is shown that the fault identification rate of the noise reduction signal is significantly higher than that of original signal. No matter which kinds of signal are, the recognition rate of fault diagnosis using wavelet packet analysis is higher than that of FFT analysis. It shows the superiority of the improved particle filter and wavelet packet analysis in the stream vibration fault diagnosis.更多还原