- moneo IIoT platform
- Software architecture
- moneo Industrial AI Assistant
Industrial AI Assistant in manufacturing improves OEE
Smart manufacturing techniques reduce downtime and increase product quality
When using Artificial Intelligence (AI) in industrial applications, relevant information is extracted from data for additional processing so that automated decisions and predictions can be made.
The Industrial AI Assistant (formerly known as moneo Data Science Toolbox) makes it easy for everyone to apply data science. This AI assistant offers various tools that you can use to optimize and monitor manufacturing processes - all without advanced knowledge of data science and programming.
Reliably monitor manufacturing processes
Simple
No data science expertise is necessary. Easy to implement for production and maintenance personnel.
Easy to train
Train your models quickly with a sample of normal operating data. No need to simulate or replicate fault conditions.
Guided configuration
Graphical template-based configurators use normal machine operating conditions to guide a user to select, test, and validate machine model performance.
Edge deployable
Compact edge algorithm models do not require powerful processors and memory, allowing processing close to the point of use.
Tunable
Fine-tune alerts as desired to optimize your preventive maintenance programs.
Reliable
Proven statistical models access both time-based and condition-based monitoring for prediction and advanced insights of changes to your equipment’s operational condition.
Industrial AI Assistant tools
The Industrial AI Assistant currently includes the software solutions moneo SmartLimitWatcher and moneo PatternMonitor which serve to detect anomalies.
Additional predictive maintenance tools are already in development and will soon be added to the Industrial AI Assistant.
 moneo SmartLimitWatcher |
 moneo PatternMonitor |
|
|---|---|---|
| Principle | Multidimensional indicators model the entire system to predict deviations or drift in the critical measurement for preventive maintenance. | Singular indicator of structural changes in individual critical process values. |
| Advantage | Automates prediction of equipment operational condition without the need to provide threshold failure limit values. | Automates detection of operational abnormalities that could signify a pending asset issue. |
| Action | Sets automated dynamic alarm thresholds based on relational measurements to predict asset failure. | Tracks anomalies in measured values to categorize operating conditions and alert of potential asset failure. |
| How? | Evaluates the most critical machine parameter by evaluating related measurements for deviations. Deviations in support variables are precursor influences on the critical measurement (target variable.) Operating changes in support variables predict changes in the target variable. | Evaluates an operating parameter for volatility, step changes, and trending. |
| Implementation requirements | Knowledge of machines to select parameters:
|
Select variable and time period |
| Uses |
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| ➜ Learn more about moneo SmartLimitWatcher | ➜ Learn more about moneo PatternMonitor |
| Industrial AI Assistant | Traditional data science approach | ||
|---|---|---|---|
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Easy-to-use tool, suitable for maintenance teams without data science skills |
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Access to data scientists necessary (expensive, $20k+) |
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Train models quickly with normal operation data |
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Difficult to find or create fault conditions requires more time and resources |
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Automatic data preparation and AI training are incorporated | |
In-house project structure and management required |
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Integrated and scalable solution within the ifm moneo system | |
Complex data acquisition systems and software development necessary |
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Suitable for a wide range of applications with quick solution and results (2 - 4 weeks) | |
Minimum project duration approx. 6 - 12 months |
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Excellent price-performance ratio |
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Increased investment risk due to limited scalability |
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Customizable options in expert mode | |
Customized solutions are difficult to scale and roll out |
