MindSet Detector is designed to monitor and detect abnormal behavior in water systems, based on several techniques:
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 to 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.
Patent pending algorithm detects changes in the variables distribution shape, sending an alert when fluctuations in the noise patterns are recognized.
Detects abnormal events based on expert rules. MindSet Detector is able to run multiple models simultaneously, and alert for each one separately.
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.
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.
In order to avoid false alarms when your system moves from one state to another, Detector monitors operational changes in process variables.
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.
Adjust for model sensitivity, or set target value for false positives and false negatives.
Classify events as Hazard, Non-hazard, Maintenance, or Instruments Malfunctioning, in order to improve model performance.
The Spatial Model is an EDS module that enables the User to monitor abnormal events on a network scale. The Spatial Model uses statistical methods utilizing the relations of measurements between different stations.
The EDS is able to monitor low-energy sensors, broadcasting only once every few hours. Low-energy sensors have thresholds, stating when water quality is hazardous. When these thresholds are violated, the low-energy sensor leaves its dormant state and begins broadcasting water quality data. The EDS allows on-line, calibration of these thresholds, making sure thresholds change dynamically with the state of the water network, season of the year, and different sensor calibrations.
Identifying pending problems in industrial systems is accomplished in many cases by detecting rare events. Detector's methodology is based on such monitoring of the existence of rare events, i.e. combinations of data that have not seen before. Rare events may be detected both on distance based or density based approaches.
These may give insight into pending problems.