孙锋, 刘杰, 周建辉, 杨梓辉, 毛伟兰. 轴系测试数据分布特征信息获取方法与应用[J]. 中国舰船研究, 2019, 14(S1): 183-188. DOI: 10.19693/j.issn.1673-3185.01466
引用本文: 孙锋, 刘杰, 周建辉, 杨梓辉, 毛伟兰. 轴系测试数据分布特征信息获取方法与应用[J]. 中国舰船研究, 2019, 14(S1): 183-188. DOI: 10.19693/j.issn.1673-3185.01466
shu
Sun Feng, Liu Jie, Zhou Jianhui, Yang Zihui, Mao Weilan. Acquisition and application of distribution characteristic information of shafting test data[J]. Chinese Journal of Ship Research, 2019, 14(S1): 183-188. DOI: 10.19693/j.issn.1673-3185.01466
Citation: Sun Feng, Liu Jie, Zhou Jianhui, Yang Zihui, Mao Weilan. Acquisition and application of distribution characteristic information of shafting test data[J]. Chinese Journal of Ship Research, 2019, 14(S1): 183-188. DOI: 10.19693/j.issn.1673-3185.01466
shu

轴系测试数据分布特征信息获取方法与应用

Acquisition and application of distribution characteristic information of shafting test data

    摘要
  • 摘要:
      目的  船舶轴系监测系统具有监控对象多、测试数据量大和存储空间需求大等特点，如果存储方案不合理，数据未预先统计和分类存储，会导致数据不便于检索、计算和分析。为此，提出针对测试数据的批量统计特征数据及分表存储的方法。
      方法  在现有数据库的基础上，设计时间序列特征数据表和汇总表，以及统计测试数据的均值、极值、标准差、偏度等的流程；采用仿真对比，选择均值和偏度作为特征向量；通过DBSCAN聚类设计传感器数据异常识别算法，验证对传感器系统异常数据的识别效果。
      结果  结果表明，所提方法对异常数据的识别效果较好，
      结论  可适用于实际测试系统的特征数据提取。

     

    Abstract:
      Objectives  The ship shafting monitoring system has many characteristics such as many monitoring objects, large amount of test data, and large storage space requirements. If there is no reasonable storage scheme and the data are not pre-counted and classified, it will cause inconvenience in data retrieval, calculation and analysis. In this end, a method for batch statistics and storage in different tables of characteristic data is proposed in this paper.
      Methods  Firstly, based on the existing database, the time series characteristic data table and the summary table were designed, and a statistical method for extracting the value of the mean, extreme variance and skewness of the test data was designed. Then, through simulation comparison, the mean and skewness of the test data were selected as characteristic vectors, and the sensor anomaly recognition method was designed by DBSCAN(Density-Based Spatial Clustering of Application with Noise) clustering algorithm so as to validate the effectiveness of recognition.
      Results  The results show that the proposed method has a good recognition effect on the abnormal data of the sensor system.
      Conclusions  The method can be used to extractcharacteristic data in practical application.

     

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