Abstract:

Currently， applying machine learning techniques to anomaly detection of drainage pipe defects， e.g.， pipe leakage， has become the focus of urban intelligent management. However， monitoring data of drainage pipe network collected from the real-world scenarios contains much noise， particularly sudden water level fluctuations caused by rainfall， significantly reduce the accuracy and reliability of detect results. To address the above problems， a robust unsupervised multi-task anomaly detection method was proposed for drainage network defect diagnosis. Firstly， by integrating the spatial-temporal information from multiple physical monitoring stations， a deep multi-task Support Vector Data Description （SVDD） model was established， individual hypersphere-based one-class classifiers were established for each station to extract anomaly detection rules， thus constructing a rule adaptation mechanism to obtain the common feature representation of multiple stations. Secondly， based on the obtained feature representations， sliding windows were introduced into each station’s SVDD model to continuously identify abnormal fluctuations in the pipeline monitoring data， thereby determining noise points caused by common interference factors in the monitoring data sequences. These noise points were corrected by polynomial interpolation to exclude irregular noise interference caused by rainfall. Finally， the modified monitoring sequences were employed to detect pipe network leakage based on AutoEncoder （AE） reconstruction errors. Experimental results on the real-world monitoring data collected from 2017 to 2018 by Qingtan Water Management System in Changzhou City demonstrate that the proposed method is consistent with the hand-operated maintenance records， moreover， the proposed method has higher detection accuracy and lower false alarm rate compared with statistical methods and traditional machine learning approaches. Taking the Qingtan East area as an example， the false detection rate of the method proposed in this paper when dealing with rainfall interference is 5.47 percentage points lower than that of the suboptimal method USAD （UnSupervised Anomaly Detection）， significantly improving the robustness of the model in strong noise scenarios and further verifying the accuracy and practicability of the proposed method.

Key words: drainage pipe network, anomaly detection, time series, multi-task learning, Support Vector Data Description (SVDD) model

摘要：

目前利用机器学习技术对城市排水管网渗漏等典型缺陷状态检测异常已成为城市智能管理的焦点；但实际场景下采集的管网监测数据包含了大量噪声，尤其是降雨造成的液位数据突变，会严重影响管网渗漏检测结果的准确性和可靠性。为解决上述问题，提出一种面向排水管网缺陷诊断的鲁棒无监督多任务异常检测方法。首先，构建融合多个物理监测站点时空信息的深度多任务支持向量数据描述（SVDD）模型，针对各站点分别建立基于超球的单分类判别器，以提取各站点异常检测规则，并建立规则适配机制，获得多个站点的公共特征表示；其次，基于所获得的特征表示，对各站点的SVDD模型进一步引入滑动窗口，连续识别管网监测数据中的异常波动，进而确定管网监测数据序列中公共干扰因素造成的噪声点，并对噪声点进行多项式插值修正，由此排除降雨等产生的不规则噪声干扰；最后，使用修正后的监测序列进行基于自编码器（AE）重构误差的管网渗漏检测。利用常州市清潭水务管理系统采集的2017—2018年城区排水管网监测数据进行验证，结果显示，所提方法和人工检修结果相符合，同时相较于基于统计方法和传统机器学习方法，检测结果更准确，误检率更低。以清潭东区域为例，该方法在应对降雨干扰时的误检率较次优方法USAD（Unsupervised Anomaly Detection）降低了5.47个百分点，显著提升了模型在强噪声场景下的鲁棒性，进一步验证了所提方法的准确性与实用性。

关键词: 排水管网, 异常检测, 时间序列, 多任务学习, 支持向量数据描述模型