Research on Deep Auto Encoding Support Vector Data Description Model for Power Equipment Anomaly Detection

doi:10.3969/j.issn.1008-0198.2024.01.017

Hunan Electric Power ›› 2024, Vol. 44 ›› Issue (1): 119-127.doi: 10.3969/j.issn.1008-0198.2024.01.017

• Faults and Analysis • Previous Articles Next Articles

Research on Deep Auto Encoding Support Vector Data Description Model for Power Equipment Anomaly Detection

GENG Bo¹, PAN Shuhui², DONG Xiaoxu³

1. Research Institute of Nuclear Power Operation, Wuhan 430223, China;
2. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;
3. School of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, China

Received:2023-09-07 Revised:2023-10-09 Online:2024-02-25 Published:2024-03-11

Abstract

Abstract: In response to the problem of insufficient ability of deep auto encoding support vector data description model to distinguish some anomalies of power equipment, this paper proposes deep auto encoding support vector data description model with self-supervised and mixture-of-experts.The multiple self-supervised transformation datasets is constructed to simulate potential unknown anomalies, and the self-supervised classification and mask reconstruction tasks is introduced to learn more discriminative representations. In addition, the encoder is transformed into a mixture-of-experts structure, and the data is allocated to different expert sub-modules for professional learning, making the abnormal decision boundary clearer. Experimental results from four public data sets and three power plant equipment data sets prove the effectiveness of self-supervised learning and mixture-of-experts.

Key words: anomaly detection, deep auto encoding support vector data description, self-supervised learning, mixture-of-experts

CLC Number:

TM762

GENG Bo, PAN Shuhui, DONG Xiaoxu. Research on Deep Auto Encoding Support Vector Data Description Model for Power Equipment Anomaly Detection[J]. Hunan Electric Power, 2024, 44(1): 119-127.

References

[1] LENNOX B, MONTAGUE G A, HIDEN H G, et al. Process monitoring of an industrial fed-batch fermentation[J]. Biotechnology and Bioengineering, 2001, 74(2):125-135.
[2] 本刊综合.党的十八大以来工业和信息化发展成就[J]. 中国信息化, 2022(6):17-23.
[3] CHEN J H, SATHE S, AGGARWAL C, et al. Outlier detection with autoencoder ensembles[M]//Proceedings of the 2017 SIAM International Conference on Data Mining.philadelphia,PA:Society for Industrial and Applied Mathematics,2017:90-98.
[4] NGUYEN M N, VIEN N A. Scalable and interpretable one-class SVMS with deep learning and random fourier features[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases.cham:Springer,2019:157-172.
[5] SCHÖLKOPF B, PLATT J C, SHAWE-TAYLOR J, et al. Estimating the support of a high-dimensional distribution[J]. Neural Computation, 2001, 13(7):1443-1471.
[6] RUFF L, VANDERMEULEN R, GOERNITZ N, et al. Deep one-class classification[C]//International Conference on Machine Learning. Stockholm, 2018:4393-4402.
[7] ZONG B, SONG Q, MIN M R, et al. Deep autoencoding Gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations. BC, 2018:1-19.
[8] CHEN Y, ASHIZAWA N, YEO C K, et al. Multi-scale self-organizing map assisted deep autoencoding gaussian mixture model for unsupervised intrusion detection[J]. Knowledge-Based Systems, 2021, 224(1):107086.
[9] WANG H, PANG G, SHEN C, et al. Unsupervised representation learning by predicting random distances[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence.Yokohama,Japan.California:International Joint Conferences on Artificial Intelligence Organization,2020:2950-2956.
[10] PANG G S, CAO L B, CHEN L, et al. Learning representations of ultrahigh-dimensional data for random distance-based outlier detection[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. London,United Kindom New York:ACM,2018:2041-2050.
[11] LIU Y Z, LI Z, ZHOU C, et al. Generative adversarial active learning for unsupervised outlier detection[J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 32(8):1517-1528.
[12] NGO P C, WINARTO A A, KOU C K L, et al. Fence GAN: towards better anomaly detection[C]//2019 IEEE 31st international conference on tools with artificial intelligence (ICTAI).Portland,OR,USA,IEEE,2020:141-148.
[13] HOJJATI H, ARMANFARD N. Dasvdd: deep autoencoding support vector data descriptor for anomaly detection[J].IEEE Transactions on Knowledge and Data Engineering,2023(10):1-12.
[14] ZHANG Z, DENG X G. Anomaly detection using improved deep SVDD model with data structure preservation[J]. Pattern Recognition Letters, 2021, 148:1-6.
[15] HOJJATI H, HO T K K, ARMANFARD N. Self-Supervised anomaly detection in computer vision and beyond: A survey and outlook[J]. Neural Networks,2024(172):106106.
[16] ZHAO Y E, ZHENG G Q, MUKHERJEE S, et al. Admoe: Anomaly detection with mixture-of-experts from noisy labels[J]//Proceedings of the AAAI Conference on Artificial Intelligence. Washington, 2023,37(4):4937-4945.
[17] SCHULZE J-P, SPERL P, BÖTTINGER K. Anomaly detection by recombining gated unsupervised experts[C]//2022 International Joint Conference on Neural Networks (IJCNN). Padua, Italy.IEEE,2022:1-8.
[18] LI Z, ZHAO Y, BOTTA N, et al. COPOD: copula-based outlier detection[C]//2020 IEEE International Conference on Data Mining (ICDM). Sorrento,Italy.IEEE,2021:1118-1123.
[19] HAN S Q, HU X Y, HUANG H L, et al. Adbench: Anomaly detection benchmark[J]. SSRN Electronics Journal,2022, 35:32142-32159.
[20] ASUNCION A, NEWMAN D. UCI machine learning repository[EB/OL] (2007-01-01)[2023-09-04].http://www.ics.uci.edu/~mlearn/MLRepository.html.

Research on Deep Auto Encoding Support Vector Data Description Model for Power Equipment Anomaly Detection

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	YIN Chaoyong, LI Gang, LI Hui, XU Biao, LIU Weiliang, XIONG Shangfeng. Principle and Implementation Method of Remote Standby Automation in Substation Based on 5G Communication Mode [J]. Hunan Electric Power, 2023, 43(1): 58-62.
[2]	LONG Xuemei, XU Liqiang, XU Biao, OUYANG Fan, LI Hui. Research on Strategy and Allocation of Standardized Security and Stability Control System [J]. Hunan Electric Power, 2022, 42(6): 104-109.