本文選題：間歇過程　切入點(diǎn)：D-S證據(jù)理論　出處：《化工學(xué)報(bào)》2017年08期

【摘要】：間歇過程測量數(shù)據(jù)的高維、非線性、非高斯分布特征直接影響過程測量數(shù)據(jù)異常檢測的準(zhǔn)確性,為了融合多源數(shù)據(jù)異常檢測信息,提升間歇過程測量數(shù)據(jù)異常檢測精度,提出了一種基于多證據(jù)融合決策的間歇過程測量數(shù)據(jù)異常檢測方法,該方法通過引入證據(jù)理論(Dempster-Shafer,D-S),采用主焦元判別偽證據(jù)和重新計(jì)算證據(jù)權(quán)重改進(jìn)沖突證據(jù)處理方法,減小了沖突證據(jù)對(duì)多證據(jù)融合決策結(jié)果的影響,提高了間歇過程測量數(shù)據(jù)異常檢測的準(zhǔn)確率。構(gòu)建了基于多證據(jù)融合的測量數(shù)據(jù)異常檢測模型并將其應(yīng)用到間歇過程測量數(shù)據(jù)異常檢測決策判決中。實(shí)驗(yàn)結(jié)果表明,該方法能夠融合多證據(jù)信息,有效地處理沖突證據(jù),實(shí)現(xiàn)了間歇過程測量數(shù)據(jù)異常檢測,降低了誤檢和漏檢率。
[Abstract]:The high dimensional, nonlinear and non-#china_person0# distribution characteristics of batch process measurement data directly affect the accuracy of process measurement data anomaly detection. In order to fuse multi-source data anomaly detection information, improve the accuracy of intermittent process measurement data anomaly detection. An anomaly detection method for intermittent process measurement data based on multi-evidence fusion decision is proposed in this paper. By introducing evidence theory into Dempster-Shafern D-Sine, the main focus element is used to distinguish false evidence and the weight of evidence is recalculated to improve the method of dealing with conflicting evidence. It reduces the impact of conflict evidence on the decision results of multi-evidence fusion. The anomaly detection model based on multi-evidence fusion is constructed and applied to the decision decision of interval process measurement data anomaly detection. The experimental results show that, This method can fuse multi-evidence information, deal with conflict evidence effectively, realize abnormal detection of intermittent process measurement data, and reduce the rate of false detection and missed detection.
【作者單位】： 北京化工大學(xué)信息科學(xué)與技術(shù)學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61240047) 北京市自然科學(xué)基金項(xiàng)目(4152041)~~
【分類號(hào)】：TQ050.7

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本文編號(hào)：1696603