基於解構式生成對抗網路於多變數工業傳感資料之異常檢測

Anomaly Detection for Multivariate Industrial Sensor Data via Decoupled Generative Adversarial Network

簡暐晉(Wei-Chin Chien)

指導教授 ： 王勝德

國立臺灣大學/電機資訊學院/電機工程學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202200380

摘要

在工業控制系統中，時常產生具有時間序列關係的數據。這些數據可能來自於橋樑震動，配水系統，或製造設備的監測數據。本文提出一個可預測多變數時間序列的異常偵測模型與訓練方法。該方法基於自編碼器，並且近一步使用生成式對抗網路，結合由生成模型產生殘差，以及判别模型輔助產生的異常分數，以增加預測精準度。所提出的演算法也降低了訓練生成式對抗網路的難度，並且該方法在SWaT, BATADAL, 以及Rare Event Classification 資料集上均比常見方法在F1 socre上取得了更好的表現。

關鍵字

異常偵測 ； 時間序列 ； 自編碼器 ； 生成對抗網路

並列摘要

Industrial control systems often contain sensor and actuator devices, which provide monitoring data in the form of time series, such as bridge vibrations, water distribution systems, and human physiological data. This thesis proposes an anomaly detection model based on an autoencoder that can consider time-series relations of the data. Moreover, the quality of the decoder output is further improved by adding a residual produced by an extra generator and discriminator. The proposed autoencoder-GAN model and detection algorithm not only improved the performance but also made the training process of GAN easier. The proposed deep learning model with the anomaly detection algorithm has been shown to achieve better results on the SWaT, BATADAL, and Rare Event Classification datasets over common methods.

並列關鍵字

Anomaly Detection ； Time Series ； Autoencoder ； Generative Adversarial Network

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