The SmartLimitWatcher is the first tool of the moneo Data Science Toolbox, which offers solutions for production based on artificial intelligence. Users benefit from the permanent monitoring of the critical process value (target variable) with regard to the production quality or the plant condition (e.g. temperature, flow, vibration, current consumption). Anomalies in the target variable are detected automatically and at an early stage.
The SmartLimitWatcher is trained using historical data, which allows a permanent, reliable target/actual comparison between measured and predicted target value. The additional calculation of dynamic expectation ranges (confidence bands) for the target variable allows the permanent evaluation of the measured behaviour of the target variable as well as the automatic indication of deviations.
In contrast to static process value monitoring, with dynamic limit value monitoring the limit values depend on the current process state of the machine or system. Support variables describe the process state of the machine or system. Using a mathematical model, the dynamic limit values are calculated based on these support variables. In the event of a deviation (anomaly), a warning or alarm is automatically issued.
The SmartLimitWatcher’s AI can be used in different ways for process monitoring. On the one hand for monitoring comparable machine components and on the other for monitoring individual add-on parts or measured variables.
Monitoring based on comparable machine components.
Note on horizontal use
The connected machine components are integrated in a process or in the same plant and a physical dependency exists. An advantage is that you need but a few sensors or measured values to detect anomalies.
Detailed monitoring of a component using several measured values.