At a large chemical production site in Germany, river water pumps must remain reliably available as part of the site’s utility infrastructure. They support key cooling and process applications and are among the assets whose condition needs to be assessed continuously during operation. To detect failures earlier and plan maintenance more precisely, a solution was needed that would allow rotating equipment to be monitored beyond fixed service intervals and manual inspections.
The implementation is carried out jointly by Currenta Conneqtive and akenza. Currenta Conneqtive deploys the solution in the field, connects the appropriate sensors, and provides LoRaWAN connectivity. akenza provides the IoT platform. It serves as the central layer where sensor data is collected, processed, visualized in dashboards, and, where required, transferred to other systems. This creates not an isolated solution for individual pumps, but a scalable platform foundation for predictive maintenance applications in industrial environments.
The challenge: Continuous condition assessment for rotating equipment
The challenge is not maintenance alone, but the need to assess the actual condition of the equipment reliably during operation. Wear and mechanical anomalies do not develop according to a fixed schedule; they depend on load, usage, and real operating conditions.
This is precisely where a purely interval-based approach reaches its limits. Fixed maintenance cycles provide planning certainty, but they do not offer a continuous view of how the condition of individual pumps changes between service interventions. At the same time, field assessment relied heavily on spot checks, practical experience, and on-site observations. While this is valuable for an initial assessment, it is only of limited use when critical rotating equipment needs to be monitored continuously.
As a result, operations lacked an ongoing data basis for identifying changes at an early stage and distinguishing normal operating behavior from emerging anomalies. Mechanical deviations such as elevated vibration levels, imbalance, or bearing play often only become clearly visible once the need for action is already increasing. In such situations, root cause analysis can only be based to a limited extent on real historical data, and maintenance decisions may have to be made without sufficiently reliable condition information.
A solution was therefore needed that would allow temperature and vibration data to be captured continuously at the pumps in order to make the actual machine condition more transparent, align maintenance measures more precisely, and reduce the risk of unplanned downtime.
The solution: Retrofit sensors and an IoT platform for pump monitoring
For the implementation, Currenta Conneqtive retrofits the river water pumps with battery-powered, ATEX-certified sensors. In the use case described here, three pumps were equipped with a total of six sensors. The sensors capture temperature and vibration data directly at the machines.
The measured values are transmitted via LoRaWAN. Currenta Conneqtive is responsible for the technical implementation in the field, including sensor integration and infrastructure connectivity.
The sensor data is centrally collected and processed on the akenza IoT platform. There, it is made available for monitoring in a dashboard and can be transferred to other systems where needed. akenza therefore provides the platform layer between sensors, connectivity, and application.
Additionally, the akenza rule engine can be used to create custom alerts and trigger automated reactions whenever sensor values exceed defined thresholds. The solution is based on a retrofit approach, allowing existing pumps to be integrated into the monitoring setup without extensive cabling. The combination of retrofit sensors, LoRaWAN, and an IoT platform forms the technical foundation of the application.
The result: Condition data as a basis for maintenance decisions
By continuously capturing temperature and vibration data, the condition of the river water pumps becomes visible on an ongoing basis rather than only at isolated points in time. Changes in machine behavior can therefore be identified earlier and interpreted more reliably during operation. This creates a more robust basis for maintenance decisions.
Instead of evaluating pump condition solely through fixed service intervals or individual on-site observations, anomalies can be examined more specifically and actions can be planned according to actual need. At the same time, root cause analyses can rely more strongly on real operating data. In operational terms, this means deviations can be reviewed earlier, actions can be planned more precisely, and interventions can be aligned more closely with the actual condition of the pumps.
For the site, this means greater transparency into the condition of critical pump infrastructure and a stronger basis for reducing unplanned downtime and avoiding consequential damage at an earlier stage.
The akenza IoT platform creates the continuous data basis required to assess the condition of critical pumps on an ongoing basis and to use that data for maintenance and further operational applications. Currenta Conneqtive turns this data basis into an operational monitoring solution by combining sensors, LoRaWAN connectivity, and field implementation into a usable setup.
For the use case described, the following KPIs were reported:
- 70% less downtime*
- 30% longer service life*
- 16 weeks from kick-off to go-live
This use case shows how predictive maintenance for rotating equipment can be brought into existing industrial environments using retrofit sensors, LoRaWAN, and a centralized IoT platform.
* Study by the World Economic Forum in collaboration with Accenture
