moneo Industrial AI Assistant

A mathematical model that predicts deviations of a system by evaluating the relation between its components, this is completed through the following simple steps:

1. Collect training data
2. Perform analysis and predict target values
3. Create dynamic thresholds
4. Take action

A statistical model that monitors operational changes of a critical process value over time, in any of these pattern types:

1. Volatility
2. Trend
3. Step Changes

• Automatically creates dynamic thresholds for the operator
• Early detection of anomalies and deviations to plan downtime appropriately

• Automatically detect patterns of a process
• Provides indicators of process quality

• Predicts remaining lifetime of wear parts
• Reduces unnecessary maintenance
• Automates forecasts without requiring data science knowledge

Actively evaluates the relation between the system’s key parameters (Target and Support variables), and compares it to the predicted target values.

These dynamic thresholds provide the operator with insights to the machine’s performance under different operating conditions.

Actively evaluates the primary process value for pattern changes, and provides indicators of slow wear (volatility and trends) or spontaneous breakdown events (step changes).

These pattern changes alert the operator of changes in process quality or asset failure.

Continuously monitors wear indicators and uses AI to predict future development based on historical data (more than 2 cycles) or statistical extrapolation (less than 2 cycles).

Live updates, confidence intervals, and interactive dashboards help plan maintenance accurately and automatically.

• Target variable – Primary measurement for predicting issues.
• Support variables – Assisting measurements influencing the system and target variable.

Knowledge of the process to select the appropriate:

• Critical process variable
• Evaluation period (time period to complete the process)

Predicts failure in cyclically worn parts to optimize maintenance planning, reduce manual checks and energy waste, and improve reliability across wear-dependent systems like filters and rotating equipment.