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moneo DataScience Toolbox: the artificial intelligence

Intelligent monitoring and optimisation of manufacturing processes

Using dependent sensor data and process parameters as a basis, SmartLimitWatcher, the intelligent tool of the moneo Data Science Toolbox, enables the automatic and early detection of anomalies in a critical process value. Its purpose is to monitor the critical process value (target variable) with regard to the production quality or the plant condition (e.g. temperature, flow, vibration, current consumption) on an ongoing basis.

With the help of AI methods, a mathematical model based on historical data is trained which is used for a permanent target/actual comparison between measured and predicted target values. Through the additional calculation of dynamic expectation ranges (confidence bands) for the target variable, the measured behaviour of the target variable can be permanently evaluated and deviations automatically indicated.

You have the possibility to define warning and alarm limits (early, medium, late), which are displayed in the application. This makes it possible to react quickly to deviations within the production process and to act proactively through optimum early detection.

moneo DataScience Toolbox – intelligent monitoring and optimisation of manufacturing processes via early warnings and alarms

  • Simple: No data science expertise necessary. Pragmatic solution with simple 5-step wizard for production and maintenance managers.
  • User-friendly: Automated data preparation and quality check. No complex data preprocessing necessary.
  • Intelligent: Selection of the best-fit AI model. Automatic model training and verification of monitoring accuracy.
  • Reliable: Time-based and condition-based monitoring. Permanent background monitoring using dynamic expectation ranges for the target variable.
  • Individual: Customisable warnings and alarms. Sensitivity of the anomaly detection can be set.

5 simple steps to an
intelligent monitoring system without expert support

 

Walkthrough: moneo SmartLimitWatcher

Application areas

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.

  • Horizontal application: Monitoring based on comparable machine components

Requirement:
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.

  • Vertical application: Detailed monitoring of a component using several measured values

Requirement:
In order to detect deviations, a sufficient number of measured values of the component to be monitored must be recorded.  This generalist approach is perfectly suited for a wide range of monitoring problems.

Implementation:
moneo DataScience Toolbox 
vs Implementation:
 Own data science project
  • Easy to use tool, no special data science expertise required
 
  • Access to data scientists necessary (expensive, 20k+)
  • Automatic data preparation and AI training are implemented
 
  • In-house project structure required
  • Integrated and scalable solution within the ifm moneo system
 
  • Manual data acquisition and processing process necessary
  • Suitability for a wide range of applications and quick solution availability
 
  • Minimum project duration approx. 3- 6 months
  • Excellent price-to-performance ratio
 
  • High probability of a positive project outcome
  • Individualisation options in expert mode
 
  • Customised solutions are often transferable only to a limited extent

moneo DataScience Toolbox – the artificial intelligence for intelligent monitoring and optimisation of manufacturing processes via early warnings and alarms.

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