上海交通大学学报

上海交通大学学报（自然版） ›› 2012, Vol. 46 ›› Issue (03): 404-409.

• 交通运输 • 上一篇    下一篇

实船喷水推进泵空化识别试验

苏永生,王永生，段向阳   

  1. （海军工程大学 船舶与动力学院，湖北 武汉 430033）
  • 收稿日期:2011-04-09 出版日期:2012-03-30 发布日期:2012-03-30
  • 基金资助:

    国家自然科学基金资助项目（51009144）

Research on the Cavitation Identification Experimentation of Marine Waterjet

 SU  Yong-Sheng, WANG  Yong-Sheng, DUAN  Xiang-Yang   

  1. (College of Naval Architecture and Marine Power，Naval University of Engineering， Wuhan 430033， China)
  • Received:2011-04-09 Online:2012-03-30 Published:2012-03-30

摘要： 根据空化特征，提出了利用高阶导数判别空化特征频带的方法.对喷水推进泵壳体振动信号以及进口水声信号进行高阶导数计算，然后进行均方根标准化，通过特征频段滤波后在时域中求取超过给定阈值的极值，计算其均值、均方根值、方差、歪度以及峭度指标，利用基于最小二乘支持向量机（LSSVC）对实船测试的6种空化状态进行了分类识别.与传统的反向传播（BP）和径向基函数（RBF）神经网络识别效果相比，LSSVC的分类准确率更高，程序运行时间更短.        

关键词: 喷水推进泵, 空化, 高阶导数, 最小二乘支持向量机

Abstract: According to cavitation feature, the method of distinguishing cavitation characteristic frequency band was posed by using higher order derivative. The shell of waterjet pump vibration and inlet underwater acoustic were first calculated by higher order derivative, and then normalized with the root mean square (RMS). After filtering by cavitation characteristic frequency band, a local maximum with amplitude over a predefining threshold was picked up. The mean, root mean square, variance, skewness and kurtosis as the support vector machine classification input can realize classification diagnosis of six kind of marine waterjet cavitation states. Compared with identification result of back propagation（BP）and radial basic function（RBF）neural networks, the classification precision of least squares support vector classification(LSSVC) is more higher and the program runtime is more shorter.

Key words: waterjet, cavitation, higher order derivative, least squares support vector classification

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