摘要
高压活塞隔膜泵是管道输送的最重要动力源,为了解决其内部单向阀故障的在线监测问题,提出一种基于声发射信号的小波包时频及核主元分析(KPCA)的检测方法。首先采用小波包对声发射数据进行处理,求出信号各频率段的能量值;然后采用KPCA方法对能量值在高维空间进行分解建立特征模型,利用特征模型中的SPE和T2统计量对故障信号进行检测;最后对GEHO型隔膜泵单向阀的声发射数据进行实验验证。通过与主元分析方法的比对,表明所提方法能够快速、准确地对单向阀故障进行在线检测,在高压活塞隔膜泵无损故障检测领域具有良好的应用前景。
High pressure piston diaphragm pump is the most important power source of the pipeline transportation. To solve the problem of on-line monitoring on the fault of internal piston, the authors put forward a detection method based on acoustic emission signal＇s wavelet packet frequency and Kernel Principal Component Analysis （KPCA）. Firstly, the author adopted wavelet packet to deal with the acoustic emission data to get each frequency band energy value. Secondly, the authors used KPCA to decompose the energy in high dimensional space to find the feature model, and made use of statistics SPE and T2 in feature model to make detection on fault signal. Finally, the authors conducted experiments to verify the statistics of acoustic emission of GEHO diaphragm pump＇s check valve. In comparison with the PCA method, the proposed method can make on-line monitoring on fault of internal piston fast and accurate, so it has good application prospect on the domain of the high pressure piston diaphragm pump＇s non-destructive fault detection.
出处
《计算机应用》
CSCD
北大核心
2013年第1期291-294,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(51169007)
云南省科技计划项目(2010DH004)
云南省中青年学术和技术带头人后备人才培养计划项目(2011CI017)
关键词
声发射
小波包分解
核主元分析
故障检测
acoustic emission
wavelet packet decomposition
Kernel Principal Component Analysis （KPCA）
fault detection
