用小波包样本熵提取出水泵机组各个振动状态下的样本熵值作为支持向量机（SVM）的特征向量，然后用SVM分类器进行训练，在此基础上对机组振动故障进行识别，为验证本方法的准确度，通过立式水泵机组实验平台进行大量的实验并对其做出了定量定性分析，实验结果表明这种基于小波包样本熵和SVM水泵机组振动故障诊断方法具有较高的可信度。
Abstract
Wavelet packet sample entropy is used to extract the pump units in every vibration state of sample entropy value as the feature vectors of support vector machine (SVM),and then the SVM classifier is used to classify. Then on this basis,the fault diagnosis of the unit vibration is carried out. In order to verify the effect of this method in practical production,a lot of experiments were done on the experiment platform of vertical pump units. The experimental results show that the pump unit vibration fault diagnosis method has high reliability.
关键词
特征提取 /
故障诊断 /
小波包样本熵 /
SVM /
泵机组
{{custom_keyword}} /
Key words
feature extraction /
fault diagnosis /
wavelet packet sample entropy /
SVM /
pump unit
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1]刘晓亭,冯辅周. 水电机组运行设备监测诊断技术及应用[M].北京: 中国水利水电出版社,2010.
[2]陈喜阳.水电机组状态检修中若干关键技术研究[D].武汉: 华中科技大学,2005.
[3]Ｒichman J S,Moorman J Ｒ. Physiological time-series analysis usingapproximate entropy and sample entropy [J]. American Journal ofPhysiology-Heart and Circulatory Physiology,2002,278(6) : 2 039－2 049.
[4]Ｒ Alcaraz,J J Ｒieta. A review on sample entropy applications for thenon-invasive analysis of atrial fibrillation electrocardiograms[J]. Biomedical Signal Processing and Control,2010.
[5]Ｒaffa F A,Vatta V. The dynamic stiffness method for linear rotor-bearing system[J]. ASME Journal of Vibration and Acoustics,1996,118(7) : 332－339.
[6]Pincus S M. Assessing serial irregularity and its implications for health[J]. Ann. N. Y. Acad. Sci,2002,954: 245－267.
[7]平 源. 基于支持向量机的聚类及文本分类研究[D]. 北京: 北京邮电大学 2012.
[8]彭 璐. 支持向量机分类算法研究与应用[D]. 长沙: 湖南大学2007.
[9]瞿 曌,赖 旭,盛 旺.水电机组状态检修模式探讨[J].华东电力,2008,(1) : 109－111.
[10]王洪杰,刘全忠,季天晶,等. 锅炉给水泵故障机理及诊断技术的研究[J]. 节能技术,2004,22(6) : 30－31.
[11]丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1) : 2－10.
[12]吴兴伟,王 雷,迟道才. 基于多分类 SVM 的水泵故障诊断的研究[J]. 节水灌溉,2009,(7) : 37－39.
[13]张 炜,张 磊,李 亮. 基于 GA 优化的 SVM 涡轮泵故障诊断[J].液压与气动,2009,(1) : 84－86.
[14]苟 博,黄贤武. 支持向量机多类分类方法[J]. 数据采集与处理,2006,21(3) : 334－339.
PDF(1609 KB)
鄂公网安备 42010602001274号 鄂ICP备14015464号-1