Outlier Detection During Thermal Processes Based on Improved Gaussian Mixture Model

doi:10.16182/j.issn1004731x.joss.22-0047

Abstract

Abstract:

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.

Key words: outlier detection, Gaussian mixture model, penalty constraint, thermal processes, unsupervised

CLC Number:

TH81

Zheng Wu, Yue Zhang, Ze Dong. Outlier Detection During Thermal Processes Based on Improved Gaussian Mixture Model[J]. Journal of System Simulation, 2023, 35(5): 1020-1033.

Figures/Tables 13

Table 1

Evaluation index description

变量名称	数学公式
准确率	$准确率 = 正确个数真实异常值个数$
漏检率	$漏检率 = 漏检个数真实异常值个数$
误判率	$误判率 = 误判个数真实异常值个数$

Table 1

Fig. 1

Table 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Table 3

Fig. 8

Table 4

Fig. 9

References 27

1	董泽, 贾昊. 基于EWT-LOF的热工过程数据异常值检测方法[J]. 仪器仪表学报, 2020, 41(2): 126-134.
	Dong Ze, Jia Hao. Detection Method for Abnormal Value of Thermal Process Data Based on EWT-LOF[J]. Chinese Journal of Scientific Instrument, 2020, 41(2): 126-134.
2	郜传厚, 渐令, 陈积明, 等. 复杂高炉炼铁过程的数据驱动建模及预测算法[J]. 自动化学报, 2009, 35(6): 725-730.
	Gao Chuanhou, Ling Gradually, Chen Jiming, et al. Data-Driven Modeling and Prediction Algorithm for Complex Blast Furnace Ironmaking Process[J]. Acta Automatica Sinica, 2009, 35(6): 725-730.
3	徐飞阳, 薛安成, 常乃超, 等. 电力系统同步相量异常数据检测与修复研究现状与展望[J]. 中国电机工程学报, 2021, 41(20): 6869-6886.
	Xu Feiyang, Xue Ancheng, Chang Naichao, et al. Research Status and Prospect of Synchrophasor Anomaly Data Detection and Restoration in Power Systems[J]. Chinese Journal of Electrical Engineering, 2021, 41(20): 6869-6886.
4	杨茂, 杨琼琼. 基于云分段最优熵算法的风电机组异常数据识别研究[J]. 中国电机工程学报, 2018, 38(8): 2294-2301, 2539.
	Yang Mao, Yang Qiongqiong. Research on Abnormal Data Recognition of Wind Turbines Based on Cloud Segmentation Optimal Entropy Algorithm[J]. Proceedings of the Chinese Society for Electrical Engineering, 2018, 38(8): 2294-2301, 2539.
5	闫媞锦, 夏元清, 张宏伟, 等. 一种非规则采样航空时序数据异常检测方法[J]. 航空学报, 2021, 42(4): 558-568.
	Yan Tijin, Xia Yuanqing, Zhang Hongwei, et al. Anomaly Detection Method of Irregularly Sampled Aviation Time Series Data[J]. Acta Aeronautica Sinica, 2021, 42(4): 558-568.
6	范涛. 基于钻孔瞬变电磁的煤层气压裂效果检测方法[J]. 煤炭学报, 2020, 45(9): 3195-3207.
	Fan Tao. Detection Method of Coal Seam Gas Pressure Cracking Effect Based on Borehole Transient Electromagnetic[J]. Journal of China Coal Society, 2020, 45(9): 3195-3207.
7	Zhao J, Kai L, Wei W, et al. Adaptive Fuzzy Clustering Based Anomaly Data Detection in Energy System of Steel Industry[J]. Information Sciences (S0020-0255), 2014, 259(3): 335-345.
8	窦珊, 张广宇, 熊智华. 基于LSTM时间序列重建的生产装置异常检测[J]. 化工学报, 2019, 70(2): 481-486.
	Dou Shan, Zhang Guangyu, Xiong Zhihua. Anomaly Detection of Production Equipment Based on LSTM Time Series Reconstruction[J]. CIESC Journal, 2019, 70(2): 481-486.
9	彭宇, 庞景月, 刘大同, 等. 大数据: 内涵、技术体系与展望[J]. 电子测量与仪器学报, 2015, 29(4): 469-482.
	Peng Yu, Pang Jingyue, Liu Datong, et al. Big Data: Connotation, Technical System and Prospect[J]. Journal of Electronic Measurement and Instrument, 2015, 29(4): 469-482.
10	D M auth Hawkins. Identification of Outliers[M]. South Africa: Chapman and Hall, Springer, 1980.
11	于冰洁. 基于高斯模型的异常检测算法[D]. 徐州: 中国矿业大学, 2017.
	Yu Bingjie. Anomaly Detection Algorithm Based on Gaussian Model[D]. Xuzhou: China University of Mining and Technology, 2017.
12	谢敬东, 卢浩哲, 陆池鑫, 等. 基于分阶段离群点检测的电力市场异常辨识[J]. 科学技术与工程, 2021, 21(9): 3633-3641.
	Xie Jingdong, Lu Haozhe, Lu Chixin, et al. Electricity Market Anomaly Identification Based on Staged Outlier Detection[J]. Science Technology and Engineering, 2021, 21(9): 3633-3641.
13	Ye Q, Zhao H, Li Z, et al. L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector k-Plane Clustering[J]. IEEE Transactions on Neural Networks and Learning Systems (S2162-237X), 2018, 29(9): 4494-4503.
14	徐瑶, 李栓, 韩英华. 基于CNN-GS-SVM的用户异常用电行为检测[J]. 控制工程, 2021, 28(10): 1989-1997.
	Xu Yao, Li Shuan, Han Yinghua. Detection of Abnormal Power Consumption Behavior of Users Based on CNN-GS-SVM[J]. Control Engineering, 2021, 28(10): 1989-1997.
15	韩旭东. 基于数据驱动的火电机组高压加热系统异常检测研究[J]. 华电技术, 2021, 43(8): 67-73.
	Han Xudong. Research on Abnormal Detection of High-Voltage Heating System of Thermal Power Unit Based on Data Drive[J]. Huadian Technology, 2021, 43(8): 67-73.
16	翟怡萌, 李晶, 程强, 等. 基于OCSVM的火电厂一次风机异常检测[J]. 工业控制计算机, 2019, 32(6): 102-104.
	Zhai Yimeng, Li Jing, Cheng Qiang, et al. Anomaly Detection of Primary Fan in Thermal Power Plant Based on OCSVM[J]. Industrial Control Computer, 2019, 32(6): 102-104.
17	Cho J H, Lee J M, Sang W C, et al. Fault Identification for Process Monitoring Using Kernel Principal Component Analysis[J]. Chemical Engineering Science (S0009-2509), 2005, 60(1): 279-288.
18	王小邦, 贺凯迅, 苏照阳. 融合互信息与慢特性的火电机组异常监控[J]. 控制工程, 2021, 10(1): 1-6.
	Wang Xiaobang, He Kaixun, Su Zhaoyang. Anomaly Monitoring of Thermal Power Units Integrating Mutual Information and Slow Characteristics[J]. Control Engineering, 2021, 10(1): 1-6.
19	Yc A, Ning C A, Mao C A, et al. A Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation[J]. Information Sciences (S0020-0255), 2021, 575(1): 41-45.
20	Zhang B, Shin Y C. An Adaptive Gaussian Mixture Method for Nonlinear Uncertainty Propagation in Neural Networks[J]. Neurocomputing (S0925-2312), 2021, 458(2): 170-183.
21	Shahin I, Nassif A B, Nemmour N, et al. Novel Hybrid DNN Approaches for Speaker Verification in Emotional and Stressful Talking Environments[J]. Neural Computing and Applications (S0941-0643), 2021, 33(1): 1-23.
22	燕彩蓉, 张青龙, 赵雪, 等. 基于广义高斯分布的贝叶斯概率矩阵分解方法[J]. 计算机研究与发展, 2016, 53(12): 2793-2800.
	Yan Cairong, Zhang Qinglong, Zhao Xue, et al. Bayesian Probability Matrix Factorization Method Based on Generalized Gaussian Distribution[J]. Computer Research and Development, 2016, 53(12): 2793-2800.
23	周帆, 郑常宝, 胡存刚, 等. 基于高斯混合模型的光伏发电功率概率区间预测[J]. 科学技术与工程, 2021, 21(24): 10284-10290.
	Zhou Fan, Zheng Changbao, Hu Cungang, et al. Probability Interval Prediction of Photovoltaic Power Generation Power Based on Gaussian Mixture Model[J]. Science Technology and Engineering, 2021, 21(24): 10284-10290.
24	Yu J, Chen K, Mori J, et al. A Gaussian Mixture Copula Model Based Localized Gaussian Process Regression Approach for Long-Term Wind Speed Prediction[J]. Energy (S0360-5442), 2013, 61(1): 673-686.
25	刘岩, 李文文, 周丽霞, 等. 基于高斯混合模型的光伏发电出力中高比例异常数据检测方法研究[J]. 电测与仪表, 2021, 58(9): 14-21.
	Liu Yan, Li Wenwen, Zhou Lixia, et al. Research on Abnormal Data Detection Method of Photovoltaic Power Generation Output Based on Gaussian Mixture Model[J]. Electrical Measurement and Instrumentation, 2021, 58(9): 14-21.
26	Reynolds, Gaussian Mixture Models D.A. : Encycl. Biometric Recognition[J]. Springer (S2193-1801), 2009, 10(5): 659-663.
27	Schwarz G E. Estimating the Dimension of a Model[J]. The Annals of Statistics (S0090-5364), 1978, 6(2): 461-464.

类别	数据集1	数据集2	数据集3	数据集4
1001-1050	+2	+5	+8	+10
1101-1150	-2	-5	-8	-10
3001-3050	+2	+5	+8	+10
3101-3150	-2	-5	-8	-10
6001-6050	+2	+5	+8	+10
6101-6150	-2	-5	-8	-10
9001-9050	+2	+5	+8	+10
9101-9150	-2	-5	-8	-10

误差/%	参数	5×10^-4	1×10^-4	5×10^-5	1×10^-5
±2	误判率	22/400	17/400	2/400	0
±2	漏检率	15/400	24/400	42/400	50/400
±5	误判率	15/400	2/400	0	0
±5	漏检率	0	0	0	0
±8	误判率	15/400	2/400	0	0
±8	漏检率	0	0	0	0
±10	误判率	15/400	2/400	0	0
±10	漏检率	0	0	0	0

算法	准确率	漏检率	误判率
PC-GMM	0.94	0.06	0.04
GMM	0.79	0.21	0.42
孤立森林	0.76	0.23	0.31
DBSCAN	0.71	0.29	0.12

[1]	Yifan He, Yulin He, Yongda Cai, Zhexue Huang. I-niceMO Enhanced Algorithm Based on Intersection Angel Geometry [J]. Journal of System Simulation, 2023, 35(4): 797-808.
[2]	Peipei Zhou, Xinglin Hou. An Unsupervised Deep Neural Network for Image Fusion [J]. Journal of System Simulation, 2022, 34(6): 1267-1274.
[3]	Qin Wanting, Lao Songyang, Tang Jun, Lu Cong. Hurricane Trajectory Outlier Detection Method Based on Variational Auto-encode [J]. Journal of System Simulation, 2021, 33(9): 2191-2201.
[4]	Qi Cheng, Xiong Weili. A Just-in-time Learning Soft Sensing Modeling Method Based on Bayesian Gaussian Mixture Model [J]. Journal of System Simulation, 2019, 31(8): 1555-1561.
[5]	Chen Fangfang, Ji Zhongping. Face Skin Detection and Color Transferring [J]. Journal of System Simulation, 2019, 31(7): 1377-1386.
[6]	Zhao Shuai, Shi Xudong, Xiong Weili. A Hierarchical Integrated Soft Sensing Modeling Method for Gauss Process Regression [J]. Journal of System Simulation, 2019, 31(10): 2042-2051.
[7]	Zhan Yongjie, Long Fei, Bu Yikun. Facial Expression Recognition with Independent Subspace Analysis Based Feature Learning [J]. Journal of System Simulation, 2015, 27(10): 2316-2319.