Research on deep autoencoder-based adaptive anomaly detection algorithm and its application

被引：0
|
作者：
Chen, Xiaohong [1 ,2 ,3 ]
Chen, Jiaolong [1 ,2 ]
Hu, Dongbin [1 ,2 ]
Liang, Wei [2 ,3 ]
Zhang, Weiwei [1 ,2 ,3 ]
机构：
[1] School of Business, Central South University, Changsha,410083, China
[2] Xiangjiang Laboratory, Changsha,410205, China
[3] School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha,410205, China
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2024年 / 44卷 / 08期
关键词：
With the development of deep learning; deep autoencoder has been widely applied in anomaly detection with efficient data encoding and reconstruction mechanisms. However; some existing deep autoencoder-based anomaly detection algorithms still face many problems; such as complex and diverse data distributions; high false alarm rate; and high missing alarm rate; etc. To overcome the above-mentioned problems; we propose a deep autoencoder-based adaptive anomaly detection algorithm. The algorithm utilizes an adaptive landmark filtering mechanism via density peak; which can select some normal samples with high density as candidate landmarks; aiming to discover the diversity of normal samples in the latent feature space. Subsequently; the landmark filtering mechanism is employed to filter and optimize the candidate landmarks to enhance the representativeness and sparsity of the landmarks. Furthermore; we design a novel loss function to optimize the model parameters iteratively with the aim of enhancing the correlation between normal samples and landmarks. Finally; the proposed anomaly detection algorithm is applied to a battery fault diagnosis; and the experiment results demonstrate that this work outperforms the existing anomaly detection algorithms in terms of accuracy; false alarm rate; and missing alarm rate. It can identify faulty batteries effectively; and provide the technical support and precise services for the battery fault identification and state management. © 2024 Systems Engineering Society of China. All rights reserved;
D O I：
10.12011/SETP2023-0815
中图分类号：
学科分类号：
摘要：
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页码：2718 / 2732
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