基于形態(tài)距離的真空熱試驗(yàn)數(shù)據(jù)相似性度量研究

Data similarity measurement method for shape-based distance in thermal vacuum test

摘要: 針對(duì)真空熱試驗(yàn)過程中的數(shù)據(jù)自動(dòng)化監(jiān)視需求，利用數(shù)據(jù)挖掘手段開展數(shù)據(jù)異常監(jiān)測(cè)方法研究，提出了一種改進(jìn)DTW-形態(tài)距離相似性度量算法，通過調(diào)整形態(tài)符號(hào)的計(jì)算方法，避免了數(shù)據(jù)規(guī)范化帶來的形態(tài)符號(hào)計(jì)算失真問題。對(duì)實(shí)際樣本數(shù)據(jù)相似性聚類準(zhǔn)確率進(jìn)行統(tǒng)計(jì)分析，獲得了相關(guān)參數(shù)的最佳取值范圍，達(dá)到了較高的聚類精度。

Abstract: For data automatic monitoring in the vacuum thermal test, the data abnormity detection method based on data mining is studied, and an improved DTW shape-based distance similarity measurement method is proposed. The algorithm reduces the amount of computation by the wavelet transformation and the search width limit, which avoids the distortion of the shape symbol by adjusting the calculation method. Its parameters are universal for the vacuum thermal test data and the best range of parameters is obtained through the statistical analysis of the actual sample data’s similarity clustering accuracy.

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