Dynamic modeling of failure events in preventative pipe maintenance

Urban water supply network is ubiquitous and indispensable to city dwellers, especially in the era of global urbanization. Preventative maintenance of water pipes, especially in urban-scale networks, thus becomes a vital importance. To achieve this goal, failure prediction that aims to pro-actively pinpoint those 'most-risky-to-fail' pipes becomes critical and has been attracting wide attention from government, academia, and industry. Different from classification-, regression-, or ranking-based methods, this paper adopts a point process-based framework that incorporates both the past failure event data and individual pipe-specific profile including physical, environmental, and operational covariants. In particular, based on a common wisdom of previous work that the failure event sequences typically exhibit temporal clustering distribution, we use mutual-exciting point process to model such triggering effects for different failure types. Our system is deployed as a platform commissioned by the water agency in a metropolitan city in Asia, and achieves state-of-the-art performance on an urban-scale pipe network. Our model is generic and thus can be applied to other industrial scenarios for event prediction.