A Sandia-developed, risk-assessment methodology for water focused on physical security of the utility infrastructure, but did not address detection and assessment of the impact of contamination within the water itself. CANARY was designed to meet that need for better assessment, McKenna said.
CANARY, which runs on a desktop computer, can be customized for individual water utilities, working with existing sensors and software, McKenna said.
While some utilities monitor their water using real-time sensors, many still send operators out once a week to take samples, said David Hart, the lead Sandia software developer for CANARY.
Compared to weekly samples, CANARY works at lightning speed.
"From the start of an event when a contaminant reaches the first sensor to an event alarm would be 20-40 minutes, depending on how the utility has CANARY configured," McKenna said.
The challenge for any contamination detection system is reducing the number of false alarms and making data meaningful amidst a "noisy" background of information caused by the environment and the utility infrastructure itself.
CANARY researchers used specially designed numerical algorithms to analyze data coming from multiple sensors and differentiate between natural variability and unusual patterns that indicate a problem. For example, the Multivariate-Nearest Neighbor algorithm groups data into clusters based on time and distance, explained Kate Klise, a numerical analyst at Sandia. When new data is received, CANARY decides whether it's close enough to a known cluster to be considered normal or whether it's far enough away to be deemed anomalous. In the latter case, CANARY alerts the utility operator, Klise said.
The computer program uses a moving 1.5- to two-day window of past data to detect abnormal events by comparing predicted water characteristics with current observations. Bu
|Contact: Heather Clark|
DOE/Sandia National Laboratories