Use cases of Time Series Explorer

This page highlights key use cases where the Time Series Explorer can provide valuable solutions.

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Example Use cases

The following section will describe 2 common use cases where the Time Series Explorer can provide valuable solutions.

Monitoring Production Line Efficiency

Scenario: A factory manager wants to figure out, why a drop in production quality occurred on the 10th. of December. He suspects that environmental metrics might be causing the issues. So he wants to visualize the Temperature and Humidity of the line.

Steps:

1. He selects the Asset Hierarchy for the production line and navigates to the proper node in the hierarchical view.

2. He adds the Temperature and Humidity tags to the Tag list using either the context menu or by double-clicking the tags.

3. He switches to the Historical Data View to see the temperature and humidity readings in the days leading up to the 10th. of December.

4. Compare the min, max, and average values of the Temperature tag and observe how fluctuations correlate with spikes or drops in Humidity.

5. He notices that there is a correlation between Temperature and Humidity. When the temperature rises the humidity drops and vice versa.

Visualizing data in graphs can make it easier to spot correlations between Temperature and Humidity

Outcome: The manager identifies that maintaining humidity and temperature within a specific range improves production quality and reduces downtime, enabling proactive adjustments.

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Validating Sensor Configuration in Real-Time

Scenario: An engineer has recently deployed a new sensor to monitor vibration levels on a critical piece of machinery and needs to validate its configuration.

Steps:

1. She navigates to the Asset Hierarchy where the sensor is deployed and locate the Vibration tag in the hierarchical view.

2. She adds the Vibration tag to the Tag list and switch to the Real-time Data View.

3. She starts the real-time data stream by clicking the Play button next to the tag in the Tag list.

4. She observes the live data stream to ensure the sensor is transmitting valid readings without delays or interruptions.

5. Use the timeline to compare the live stream data with historical records to verify that the sensor is configured for the correct resolution and frequency.

Outcome: The engineer confirms that the sensor is properly configured and streaming accurate data, allowing it to be integrated into predictive maintenance workflows.