基于改进高斯混合模型的热工过程异常值检测  

Outlier Detection During Thermal Processes Based on Improved Gaussian Mixture Model

作　　者：吴铮 张悦[1,2] 董泽 Wu Zheng;Zhang Yue;Dong Ze(School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China;Hebei Technology Innovation Center of Simulation&Optimized Control for Power Generation,North China Electric Power University,Baoding 071003,China)

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]河北省发电过程仿真与优化控制技术创新中心(华北电力大学),河北保定071003

出　　处：《系统仿真学报》2023年第5期1020-1033,共14页Journal of System Simulation

基　　金：中央高校基本科研业务费专项资金(2018QN096);河北省自然科学基金(E2018502111)。

摘　　要：热工过程异常数据检测是进行系统建模、控制、优化的基础,也是数据处理的重要组成部分。提出一种基于改进高斯混合模型的无监督热工过程异常值检测算法,利用每一维高斯分量捕获一类特定工况数据集群,通过添加惩罚约束因子修正传统模型的后验概率密度,对误检、漏检项加以惩罚,并根据与集群的相关性差异实现异常数据的识别。仿真实验结果表明,模型在多种误差条件下均可准确定位异常数据位置,具有很强的泛化性能,同时相较于传统高斯混合模型,误检、漏检点的检测效果总体提升了37.8%和15%,反映出模型改进的有效性。Abnormal data detection during thermal processes is the basis for performing system modeling,control,and optimization and constitutes an important part of data processing.In this paper,an unsupervised outlier detection algorithm during thermal processes based on an improved Gaussian mixture model is proposed.The algorithm captures a class of data clusters under specific working conditions by using Gaussian components in each dimension,modifies the posterior probability density of the traditional model by adding penalty constraint factors to penalize the false detection and missed detection items,and identifies abnormal data according to the correlation differences with the clusters.The simulation experimental results show that the model can accurately locate the abnormal data location under a variety of error conditions with strong generalization performance,and the overall detection effects of false detection and missed detection items are improved by 37.8%and 15%compared with the traditional Gaussian mixture model,which proves the effectiveness of the model improvement.

关 键 词：异常值检测 高斯混合模型 惩罚约束 热工过程 无监督 

分 类 号：TH81[机械工程—仪器科学与技术] TP391.9[机械工程—精密仪器及机械]