论文标题
使用深度自动编码器用于原位废水系统监测数据的异常检测
Anomaly Detection using Deep Autoencoders for in-situ Wastewater Systems Monitoring Data
论文作者
论文摘要
由于废水系统中原位传感器的数据量增加，因此有必要自动识别异常行为并确保高数据质量。本文提出了一种基于对原位废水系统监测数据的深度自动编码器的异常检测方法。自动编码器体系结构基于1D卷积神经网络（CNN）层，其中卷积是在数据的时间轴上进行的。然后根据解码阶段的重建误差进行异常检测。该方法在从隔离过程监视数据的多元时间序列上进行了验证。我们讨论了复杂时间序列中标记异常的结果和挑战。我们建议我们提出的方法可以支持识别异常的领域专家。
Due to the growing amount of data from in-situ sensors in wastewater systems, it becomes necessary to automatically identify abnormal behaviours and ensure high data quality. This paper proposes an anomaly detection method based on a deep autoencoder for in-situ wastewater systems monitoring data. The autoencoder architecture is based on 1D Convolutional Neural Network (CNN) layers where the convolutions are performed over the inputs across the temporal axis of the data. Anomaly detection is then performed based on the reconstruction error of the decoding stage. The approach is validated on multivariate time series from in-sewer process monitoring data. We discuss the results and the challenge of labelling anomalies in complex time series. We suggest that our proposed approach can support the domain experts in the identification of anomalies.
