Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Cynthia Archer, Todd Leen, António Baptista
As part of an environmental observation and forecasting system, sensors deployed in the Columbia RIver Estuary (CORIE) gather information on physical dynamics and changes in estuary habi- tat. Of these, salinity sensors are particularly susceptible to bio- fouling, which gradually degrades sensor response and corrupts crit- ical data. Automatic fault detectors have the capability to identify bio-fouling early and minimize data loss. Complicating the devel- opment of discriminatory classi(cid:12)ers is the scarcity of bio-fouling onset examples and the variability of the bio-fouling signature. To solve these problems, we take a novelty detection approach that incorporates a parameterized bio-fouling model. These detectors identify the occurrence of bio-fouling, and its onset time as reliably as human experts. Real-time detectors installed during the sum- mer of 2001 produced no false alarms, yet detected all episodes of sensor degradation before the (cid:12)eld sta(cid:11) scheduled these sensors for cleaning. From this initial deployment through February 2003, our bio-fouling detectors have essentially doubled the amount of useful data coming from the CORIE sensors.