本文選題：車輛中時 + 異常檢測��； 參考：《北京理工大學(xué)學(xué)報》2017年11期

【摘要】：針對鐵路車輛在站中轉(zhuǎn)作業(yè)異常較多的情況,提出基于BIRCH-LKD的在站車輛中時異常檢測算法.該算法以車輛中時序列為研究對象,不考慮異常值的具體形式,對序列分組,引入中時序列特征向量,做類球形簇轉(zhuǎn)化;采用基于劃分的顯性異常檢測方法得到中時序列特征向量的聚類特征樹,查找序列顯性異常,縮小異常檢測范圍;利用隱性異常檢測算法計算剩余數(shù)據(jù)對象的K距離,根據(jù)距離差值變化規(guī)律,篩選序列隱性異常;最后,利用中時序列中位數(shù)異常判定條件,排除下界異常,實現(xiàn)中時序列的異常檢測.實驗結(jié)果表明,該算法檢出率高,能夠快速識別中時序列異常值,有效率達(dá)85%以上,去除異常值后的中時序列符合實際情況的趨勢且更加平穩(wěn).
[Abstract]:A BIRCH-LKD algorithm based on BIRCH-LKD is proposed to detect abnormal railway vehicles in station. The algorithm takes the vehicle time series as the research object, does not consider the concrete form of the outliers, and introduces the middle time sequence feature vector to transform the sequence into spherical clusters. Based on the explicit anomaly detection method based on partition, the clustering feature tree of middle time sequence feature vector is obtained to find the sequence dominant anomaly and narrow the range of anomaly detection, and to calculate the K distance of the remaining data object by using the hidden anomaly detection algorithm. According to the variation rule of distance difference, the hidden anomaly of the sequence is screened. Finally, the anomaly detection of the middle time sequence is realized by using the condition of the median anomaly of the middle time sequence to exclude the lower bound anomaly. The experimental results show that the algorithm has a high detection rate and can quickly identify the outliers of middle time series, and the effective rate is more than 85%. The time series after removing the outliers are in line with the trend of the actual situation and are more stable.
【作者單位】： 北京交通大學(xué)交通運輸學(xué)院;中國鐵路總公司信息技術(shù)中心;
【基金】：中國鐵路總公司(省部級)科技研究開發(fā)計劃課題(2014X009-A)
【分類號】：TP311.13;U292.11
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本文編號：2056942