The Real-Time Intelligence (RTI) Operations Dashboard provides instant visibility into the health and performance of key assets at a manufacturing facility located at Contoso Outdoors – Ho Chi Minh Facility. It monitors two critical assets:
- Robotic Arm 1 (A_1000)
- Packaging Line 1 (A_1001)
The dashboard uses live sensor data, Z-score-based anomaly detection, and trend analysis to help teams identify issues early, prevent downtime, and improve product quality. For more information about the statistical z-score analysis, please refer to Data Analysis Guide for the details.
The dashboard's effectiveness depends on the quality of the underlying dataset. To ensure accurate analysis using the sample data provided by this solution accelerator:
- Refresh Historical Data: Follow the Fabric Data Ingestion Guide to load baseline data. Use the options
--refresh-datesand--overwriteto get a fresh and up-to-date dataset. - Start Real-Time Simulation: Use the Event Simulator Guide to generate streaming telemetry.
The event simulator streams real-time telemetry data into the Fabric Event House, which serves as the data source for the RTI Operations Dashboard. It supports multiple operational modes: Normal Mode and Enhanced Anomaly Mode..
The Real-Time Intelligence Operations Dashboard is a two-page interactive dashboard providing comprehensive asset performance monitoring. Page 1 shows real-time sensor status, quality metrics, and trends; Page 2 provides advanced analytics including anomaly correlations and maintenance insights.
Business Value:
- Real-Time Monitoring: Instant visibility into equipment health and performance across all critical assets
- Early Issue Detection: Statistical anomaly detection identifies problems before they cause downtime
- Data-Driven Decisions: Z-score analysis provides objective context for operational decisions
- Risk Prioritization: Color-coded alerts help teams focus resources on the most critical issues first
This page serves as the main monitoring view. It shows current equipment health, sensor status, and key performance trends.
Displays latest sensor readings with real-time anomaly status. Each row shows Speed, Temperature, Vibration, and Defect Probability values with Z-score-based health indicators.
Side-by-side comparison of anomaly rates and quality issues between assets in a column chart format. Uses Z-score analysis with a 30-day baseline (excludes last 24 hours).
- Key Metrics Displayed:
- Anomaly Rate %: Combined percentage of speed, temperature, and vibration anomalies
- Quality Issues %: Percentage of events with defect probability exceeding 5%
The four Trend charts display time-series trends for each sensor metric (Speed, Temperature, Vibration) and predicted Defect Probability based on the combined sensor values. Each trend chart helps identify patterns, gradual drift, or sudden changes in equipment behavior.
This page provides deeper insights into operational patterns, correlations, and maintenance status for advanced troubleshooting and strategic planning.
Displays the overall anomaly rate percentage over time per day, showing the combined rate of speed, temperature, vibration, and quality anomalies as a percentage of total events.
This table highlights how often different sensor metrics experience anomalies together. In other words, it answers:
- When Speed shows an anomaly, how likely is Temperature also abnormal?
- Do vibration issues often occur alongside quality problems?
Columns Explained:
- Asset: Name of the equipment being monitored
- Speed→Temp: How often speed anomalies coincide with temperature anomalies
- Speed→Vibration: Indicates if speed fluctuations are linked to mechanical stress
- Speed→Quality: Shows whether abnormal speed impacts defect probability
- Temp→Vibration: Correlation between thermal issues and vibration problems
- Temp→Quality: Highlights if overheating affects product quality
- Vibration→Quality: Relationship between mechanical health and manufacturing precision
- Total Anomalies: Combined count of all anomaly events for the asset
Example Interpretations:
- High Speed→Vibration = Speed variations causing mechanical stress
- High Temp→Quality = Overheating directly impacting product quality
- High Vibration→Quality = Mechanical issues affecting manufacturing precision
Displays a table showing each asset's maintenance status and last update time.
Compares anomaly rates across three work shifts to identify operational patterns and performance differences.