EDS "Mindset Detector" Server

MindSet Detector is designed to monitor and detect abnormal behavior in water systems, based on several techniques:

"Rare Combination" Alerting

The proprietary algorithm classifies historical event data, event frequency, and relevancy as Known, Unknown, Hazard, Maintenance, etc. This enables the system to detect when a new or rare combination of variables occurs and distinguish between false and real alarms.

Trend-Based Alerting

Long-term trend analysis enables the Detector to identify and alert for deterioration of equipment or processes and recommend corrective actions.

Noise-Based Alerting

The patent-pending algorithm detects changes in the variables distribution shape, sending an alert when fluctuations in the noise patterns are recognized.

Rule Engine Alerting

Detects abnormal events based on expert rules. MindSet Detector can run multiple models simultaneously and alert for each one separately.

Deploy or use as Desktop Application

MindSet Detector is built as a server, using Windows services architecture. It can be used as a stand-alone product, or be integrated in any .net system using its API. OPC connectivity enables the integration of Detector with any SCADA.

Auto classification of several event types

Communication problems, data with low quality (e.g., fixed values for an over-extended period), operational events (e.g., abnormal pressure or flow), operational changes which generate short-term disturbances to water quality, and true quality water changes.

Uses machine learning technology

Based on both public and proprietary machine learning algorithms, Detector builds a mathematical model for each selected unit that describes the relationship between inputs and outputs. No knowledge of mathematical modelling is required - models are generated automatically.

Automatic or Manual Tuning

Adjust for model sensitivity, or set target value for false positives and false negatives.

Detection of Operational Change

In order to avoid false alarms when your system moves from one state to another, Detector monitors operational changes in process variables.

User may Classify Past Events

Classify events as Hazard, Non-hazard, Maintenance, or Instruments Malfunctioning, in order to improve model performance.

 

Spatial Model

The Spatial Model is an EDS module that enables the User to monitor abnormal events on a network scale. 

Scientific Background

Identifying pending problems in industrial systems is accomplished in many cases by detecting rare events. 

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