Integrated Generative Model for Industrial Anomaly Detection via Bidirectional LSTM and Attention Mechanism
| Autor: | Bin Jiang, Fanhui Kong, Houbing Song, Jianqiang Li, Huihui Wang |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Discriminator
Computer science Generation loss computer.software_genre Computer Science Applications Generative model Control and Systems Engineering Key (cryptography) Anomaly detection Data mining Electrical and Electronic Engineering Time series Anomaly (physics) computer Information Systems Generator (mathematics) |
| Zdroj: | IEEE Transactions on Industrial Informatics. 19:541-550 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2021.3078192 |
| Popis: | For emerging Industrial Internet of Things (IIoT), intelligent anomaly detection is a key step to build smart industry. Specially, explosive time series data brings enormous challenges to the information mining and processing for modern industry. How to identify and detect the multi-dimensional industrial time series anomaly is an important issue. However, most existing studies fail to handle with large amounts of unlabeled data and get the undesirable results. In this paper, we propose a novel integrated deep generative model (DGM), which is built by generative adversarial networks based on bi-directional LSTM and attention mechanism (AMBi-GAN). The structure for generator and discriminator is the bi-directional long short-term memory with attention mechanism (AMBi-LSTM), which can capture time series dependence. Reconstruction loss and generation loss test the input of sample training space and random latent space. Experimental results show that the detection performance of our proposed AMBi-GAN has the potential to improve the detection accuracy of industrial multi-dimensional time series anomaly, towards IIoT in the era of AI. |
| Databáze: | OpenAIRE |
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