Data-driven Abnormal Detection for Utility-oriented Sensor Time Series Data

With the advancement of Internet of Things (IoT) technology, smart sensors have become extensively used in public utilities for data collection. This provides a basis for data-driven analysis methods that rely on sensor time series data. This PhD thesis focuses on utilities as the application and aims to identify anomalies in their operations through machine learning algorithms based on sensor time series data.
Firstly, to address the issue of missing values in sensor time series data, a novel time series imputation method called MuSDRI is proposed. Unlike previous methods that only concentrate on short-term features, MuSDRI captures temporal dynamics from both short-term and long-term perspectives, resulting in improved performance over state-of-the-art methods for imputation tasks. Secondly, D2AE, a data distillation-enhanced autoencoder is proposed for anomaly detection of gas consumption data. D2AE is a new unsupervised anomaly detection method designed for detecting anomalous gas consumption of different types of users and with various causes. Experimental results demonstrate that D2AE outperforms the baseline method on two gas consumption datasets. Thirdly, a cross-network Contamination Source Identification (CSI) solution for Water Distribution Networks (WDNs) called GrassL is presented. It can transfer CSI knowledge across WDNs. To the best of the author’s knowledge, GrassL is the first transfer learning method proposed for the CSI problem in WDNs. Experimental results demonstrate that GrassL achieves the best performance compared to other deep learning methods that capture temporal and spatial dynamics in the CSI task across WDNs. In conclusion, this research contributes innovative technologies and solutions for abnormal detection in public utilities, laying the groundwork for further exploration and expansion.

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