Event Detection Affordable for everyone Best available EDS Simple, easy to use and automatic

ana::tool turns your monitoring station into an Event Detection System!

ana::tool identifies unknown and unusual conditions and enables operators to react timely to faults in the monitored system, determines normality of these data and triggers an alarm when a significant deviation from normality is detected.

How does ana::tool work?

ana::tool evaluates measurement data that have been cleaned by the validation module. It identifies unknown and unusual conditions and enables operators to react timely to faults in the monitored system, determines normality of these data and triggers an alarm when a significant deviation from normality is detected. It combines Static Alarms, Dynamic Alarms, Pattern Recognition and Spectral Alarms. Once an alarm is detected, the user has to provide feedback, so the system can learn what alarms are real and which ones represented normal changes in water quality. This will increase system performance over time. Gradual composition changes (e.g. seasonal variations) are accounted for by automatic training on a moving time window.

ana::tool - Highlights

• Unmatched event detection tools based on proven algorithms for real-time event detection that use data streams from all connected probes separately and in combination
• The only software developed by the market leader to be specifically capable of exploiting the enormous information contained in UV spectra which provide the most sensitive and stable data source for event detection
• ana::tool is optimized for use of multi-dimensional spectral data, but will also work with single or multiple one-dimensional inputs
• Applies a variety of algorithms during real-time analysis of data and automatically weights the results as appropriate. So far the only one commercial software package that was tested and found suitable by US-EPA water security division
• All event information is automatically aggregated into a “traffic light” output and a “% deviation from normal” output. Furthermore, analogue and digital outputs as well as text notifications can be triggered
• Trains itself on any type of data streams coming in, and will learn automatically which data are useful for event detection, and which ones not
• ­Configurable auto-correction of data based on threshold, outlier and noise analysis