Title

Failure Prediction for Large-scale Water Pipe Networks Using GNN and Temporal Failure Series

Publication Name

International Conference on Information and Knowledge Management, Proceedings

Abstract

Pipe failure prediction in the water industry aims to prioritize the pipes that are at high risk of failure for proactive maintenance. However, existing statistical or machine learning models that rely on historical failures and asset attributes can hardly leverage the structure information of pipe networks. In this work, we develop a failure prediction framework for pipe networks by jointly considering the pipes' features, the network structure, the geographical neighboring effect, and the temporal failure series. We apply a multi-hop Graph Neural Network (GNN) to failure prediction. We propose a method of constructing a geographical graph structure depending on not only the physical connections but also geographical distances between pipes. To differentiate the pipes with diverse properties, we employ an attention mechanism in the neighborhood aggregation process of each GNN layer. Also, residual connections and layer-wise aggregation are used to avoid the over-smoothing issue in deep GNNs. The historical failures exhibit a strong temporal pattern. Inspired by point process, we develop a module to learn the pipes' evolutionary effect and the time-decayed excitement of historical failures on the current state of the pipe. The proposed framework is evaluated on two real-world large-scale pipe networks. It outperforms the existing statistical, machine learning, and state-of-the-art GNN baselines. Our framework provides the water utility with core data-driven support for proactive maintenance including regular pipe inspection, pipe renewal planning, and sensor system deployment. It can be extended to other infrastructure networks in the future.

Open Access Status

This publication is not available as open access

First Page

3955

Last Page

3964

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Link to publisher version (DOI)

http://dx.doi.org/10.1145/3459637.3481918