MindSet Detector is designed to monitor and detect and classify abnormal behaviour 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.
Long-term trend analysis enables the Detector to identify and alert for deterioration of equipment or processes and recommend corrective actions.
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
Abnormal events detection 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.