Name
Abstract | ||
|---|---|---|
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 pro file 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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2806340 | IEEE ACCESS |
Keywords | Field | DocType |
Point process, pipe failure prediction, event series modeling | Pipeline transport,Ranking,Computer science,Water supply network,Risk analysis (engineering),System dynamics,Water resources,Metropolitan area,Preventive maintenance,Maintenance engineering,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Zhang | 1 | 33 | 4.39 |
| Hao Wu | 2 | 143 | 18.69 |
| Rongfang Bie | 3 | 547 | 68.23 |
| Rashid Mehmood | 4 | 355 | 45.46 |
| Anton Kos | 5 | 80 | 17.96 |