Data-Driven Maintenance in Practice: ΔP-Based Filter Monitoring on the Heller H6000 System
ΔP-based sensor-based condition monitoring on a Heller H6000 exhaust system – with real-time monitoring and predictive maintenance features.
Data-Driven Maintenance in Practice
How Did a ΔP Sensor Predict a Critical Condition in a Heller H6000 Exhaust System?
Modern industrial maintenance is no longer just about troubleshooting or periodic servicing. Industry 4.0 and real-time data collection enable us to continuously monitor the condition of equipment and identify problems before a failure occurs.
In this case study, we present a condition assessment of the exhaust system of a Heller H6000 machining center, where we used sensor measurements and data collection to visualize the system’s actual condition.
The Initial Problem
During operation of the exhaust system under examination, there was no accurate feedback available regarding the saturation status of the filters or the current efficiency of the exhaust system.
In practice, this typically leads to the following problems:
- filter clogging detected too late
- decreasing exhaust performance
- increased energy consumption
- fan overload
- unplanned failures
Measurement methodology
To determine the system’s status, we used differential pressure measurement before and after the filter, supplemented by airflow measurement.
Measured values
- Pressure before the filter
- Pressure after the filter
- Differential pressure, i.e., ΔP
- Airflow in the exhaust duct
The measurement data was fed into a central data collection system:
4–20 mA ΔP sensor → IO-Link → Balluff → MQTT → Einflux
Identification of critical conditions
During on-site measurements, we recorded an extremely high pressure difference.
| Condition | ΔP value |
|---|---|
| Clogged filter | 557 Pa |
| After cleaning | 15.8 Pa |
The high ΔP value clearly indicated that the filter was critically clogged.
After cleaning:
- airflow improved significantly
- exhaust performance was restored
- system load was reduced
The test also revealed that the fan motor had already been subjected to significant mechanical stress due to the persistently high resistance.
Real-time condition monitoring
One of the most important outcomes of the project was that the combination of sensor measurements and data collection is capable of indicating in real time:
- the filter’s saturation status
- the performance of the exhaust system
- the approaching need for maintenance
The data is displayed on a dashboard interface, where the system can generate alerts or maintenance entries based on automatic rules.
Example:
An automatic warning is triggered when the ΔP value exceeds 250 Pa.
This enables:
- scheduled maintenance
- reduction of unexpected downtime
- improved energy efficiency
- extended component lifespan
Conclusion
This case study clearly demonstrates how a seemingly simple ΔP measurement can reveal serious operational issues.
Sensor-based condition monitoring not only makes the condition of filters measurable but also facilitates predictive maintenance of the entire exhaust system.
Based on the project’s findings, the following is recommended:
- Extending data collection to additional machines
- Regular monitoring of critical fans
- Implementing a ΔP-based maintenance strategy
Results at a Glance
- 557 Pa → 15.8 Pa ΔP reduction after cleaning
- real-time filter condition monitoring
- automatic maintenance alerts
- scalable solution for additional machines
Interested in sensor-based condition monitoring?
We can help you with real-time monitoring of industrial equipment, data collection, and the development of predictive maintenance systems.