Speaker

　　Title

　　Time

　　Venue

N202

　　Abstract

It is crucial to effectively and efficiently detect bursts in water distribution systems (WDSs). This is because that WDSs are usually deployed underground, it is difficult to detect bursts before their catastrophic results, such as short-term high-flow water loss, being observed on the ground surface. Continuous hydraulic data streams collected from automatic meter reading and advanced metering infrastructure systems make it possible to detect bursts in WDS based on data analytics. Conventional statistical process control methods may not be effective. This is because that the Spatio-temporal correlations and non-stationary shifts imbedded in the data streams are not explicitly considered, leading to high rate of false alarm and/or miss detection. In this research, a burst detection method based on functional data decomposition is proposed. The spatio-temporal correlations are modeled with functional bases, and the non-stationary shifts induced by bursts are decomposed as anomalies from the data streams of customers’ daily use without bursts. The proposed method significantly reduces the rate of false alarm or miss detection. Its effectiveness is demonstrated with a case study based on numerical simulation of a real-world WDS.

　　Affiliation

Dr. Jian Liu is an Associate Professor in the Department of Systems & Industrial Engineering at The University of Arizona. Dr. Liu’s research specialty is in the fusion of multi-source, multi-scale and multilevel information in hierarchical and distributed systems for better system design, operation and maintenance. He is a member of INFORMS and a member of IISE. His research has been supported by NSF, AFOSR, among others.