How moneo SmartLimitWatcher works

An algorithm trains a mathematical model using historical data that continuously compares measured values with predicted target values, thus providing a continuous target/actual comparison. It also calculates dynamic expectation ranges (confidence bands) for the target variable to automatically detect and signal deviations. moneo SmartLimitWatcher is a smart AI monitoring tool that is easy to handle and can be operated without data science expertise. It makes data conveniently and automatically accessible, monitoring it autonomously on the basis of machine learning.

What moneo SmartLimitWatcher can do

The AI software is custom-configurable and offers user-friendly sensitivity adjustment for anomaly detection. Warning and alarm thresholds can be customised to allow you to respond quickly to deviations in your production process. You can choose from “inactive”, “low”, “medium”, “high” and “custom”. The moneo SmartLimitWatcher software solution can be used both for monitoring comparable machine components and for detailed monitoring of individual components using multiple parameters for condition monitoring.

Applications for moneo SmartLimitWatcher

The SmartLimitWatcher’s AI can be used in different ways for process monitoring within the context of condition monitoring: to monitor comparable machine components or to monitor individual add-on parts or parameters. Horizontal application: monitoring based on comparable machine components Requirement: The connected machine components are integrated in a process or in the same plant and are physically interdependent. The advantage is that only a few sensors or parameters are needed to detect anomalies. Vertical application: detailed monitoring of a single component using several parameters Requirement: In order to detect deviations, a sufficient number of parameters of the component to be monitored must be recorded.This generalist approach is very well suited to a wide range of monitoring problems.

moneo SmartLimitWatcher

moneo SmartLimitWatcher is an AI-based software product. It is used to dynamically monitor threshold values in complex machine and production processes in manufacturing. The intelligent software alerts you to anomalies in key process values such as temperature, pressure, or vibration.

Improve
machine availability

Prevent unplanned downtime by predicting failures and increasing machine availability.

Maintain
process quality

Improve process performance by monitoring and detecting deviations in real time.

Optimize
energy consumption

Reduce operating costs by monitoring energy consumption at the machine level.

SmartLimitWatcher dynamically monitors several process values and alerts you to anomalies.

Benefits of SmartLimitWatcher

Simple
No data science expertise required. Pragmatic solution with simple 5-step wizard for those responsible for production and maintenance.

Convenient
Automated data preparation, model training, and quality control. No complex data pre-processing required.

Smart
Automated selection of the best-fit AI model, including verification of monitoring accuracy.

Reliable
Time-based and condition-based monitoring. Continuous background monitoring using dynamic expectation ranges for the target variable.

Customizable
Adjustable anomaly detection sensitivity with customizable warnings and alarms.

Intelligent process monitoring:
the 5-step wizard

moneo SmartLimitWatcher offers a set-up wizard that makes the software easy to use. In just five steps, the intelligent monitoring tool can be activated without expert support.

moneo SmartLimitWatcher monitors multi-stage, complex industrial processes and machines. The software automatically detects anomalies based on sensor data such as temperature, flow and vibration and sends warnings and alarms based on dynamic thresholds.

Customers who prefer to monitor individual measured values can use moneo PatternMonitor. This AI tool identifies structural changes in critical process values. Automatically generated alarms enable appropriate measures to be initiated at an early stage.

➜ Learn more about moneo PatternMonitor