Outlier Detection During Thermal Processes Based on Improved Gaussian Mixture Model

Authors

Zheng Wu, 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2.Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;Follow
Yue Zhang, 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2.Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;
Ze Dong, 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;2.Hebei Technology Innovation Center of Simulation & Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China;Follow

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.

Recommended Citation

Wu, Zheng; Zhang, Yue; and Dong, Ze (2023) "Outlier Detection During Thermal Processes Based on Improved Gaussian Mixture Model," Journal of System Simulation: Vol. 35: Iss. 5, Article 11.
DOI: 10.16182/j.issn1004731x.joss.22-0047
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss5/11

First Page

1020

Revised Date

2022-02-18

DOI Link

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

Last Page

1033

CLC

TH81

Recommended Citation

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.

Corresponding Author

Ze Dong,dongze@ncepu.edu.cn

DOI

10.16182/j.issn1004731x.joss.22-0047