基于先驗聚類的機電設備環境參數異常檢測算法

摘" 要： 傳統的聚類異常數據檢測算法在處理高維度、大數據量且異常值分布雜亂的機電設備環境參數時，存在聚類效果差和檢測效率低的問題。為此，在原有異常檢測算法的基礎上提出一種基于先驗聚類的機電設備環境參數異常檢測算法。該算法改用歷史數據構建先驗聚類，確保聚類構建不會受太多異常環境參數所影響；在選取聚類中心時引入密集度的概念，以確保聚類中心的可靠性，并在選取聚類中心過程中去除已選聚類中心周圍的數據點，防止選取的聚類中心集中在某一區域，以此提升聚類效果。進行異常檢測時，依次將待檢測數據放入先驗聚類中進行匹配，一旦測試數據無法匹配任何一個已知聚類，則將其標記為異常數據。實驗結果表明：所提算法在機電設備環境參數的異常檢測方面具有檢測率高、誤報率低的特點，在2 000例數據異常檢測中，其檢測準確率達到了97.5%，優于DBSCAN算法的97%以及基礎K?means算法的86%；同時，誤檢率低至0.010 6，優于DBSCAN算法的0.023 9和基礎K?means算法的0.022 8。改進后的模型較基礎K?means算法和DBSCAN算法在機電設備環境參數異常檢測中檢測效果更佳，在機電設備環境異常數據檢測上具有良好的性能。

關鍵詞： 機電設備； 環境參數； 異常數據檢測； 先驗聚類；" K?means算法； 密集度； 聚類匹配

中圖分類號： TN915.08?34； TP301" " " " " " " " 文獻標識碼： A" " " " " " " " " " "文章編號： 1004?373X（2025）06?0078?07

Prior clustering based environmental parameter anomaly detection algorithm of electromechanical equipment

XING Peng， LI Xine

（State Key Laboratory of Electronic Measurement Technology， North University of China， Taiyuan 030051， China）

Abstract： The traditional clustering anomaly data detection algorithm has the problem of poor clustering effect and low detection efficiency when dealing with the environmental parameters of electromechanical equipment with high dimension， large data amount and chaotic distribution of outliers. Therefore， on the basis of the traditional anomaly detection algorithm， a prior clustering based environmental parameter anomaly detection algorithm of electromechanical equipment is proposed. In this algorithm， the historical data is used to construct prior clustering to ensure that the cluster construction cannot be affected by too many abnormal environmental parameters. The concept of density is introduced to ensure the reliability of cluster centers when selecting cluster centers， and the data points around the selected cluster centers are removed in the process of selecting cluster centers to prevent the selected cluster centers from being concentrated in a certain area， so as to improve the clustering effect. In the process of anomaly detection， the data to be detected are put into the prior clustering for matching. Once the testing data cannot match any of the known clusters， it is marked as abnormal data. The experimental results show that the proposed algorithm has the characteristics of high detection rate and low 1 positive rate in the abnormal detection of electromechanical equipment environmental parameters. In the abnormal detection of 2 000 cases of data， the detection accuracy rate can reach 97.5%， which is better than 97% of DBSCAN algorithm and 86% of basic K?means algorithm. Its 1 detection rate is as low as 0.010 6， which is better than 0.023 9 of DBSCAN algorithm and 0.022 8 of basic K?means algorithm. In comparison with basic K?means algorithm and DBSCAN algorithm， the improved model has better detection effect in the environmental parameters anomaly detection of electromechanical equipment， and has good performance in the detection of environmental abnormal data of electromechanical equipment.

Keywords： electromechanical equipment; environmental parameters; anomaly data detection; priori clustering; K?means algorithm; degree of density; cluster matching

0" 引" 言

隨著微電子、MEMS技術及無線通信技術的不斷發展，無線傳感器網絡（WSN）憑借其節點微型化、低成本及靈活部署的優勢，目前已廣泛應用于機電設備的環境參數檢測中[1]。……