Predictive maintenance: your key strategy for trouble-free and efficient production
At a time when unplanned downtimes and production losses cause immense costs and competitive disadvantages, predictive maintenance is becoming a decisive strategy for plant operators and manufacturers. Intelligent data acquisition, machine learning and continuous condition monitoring allow potential faults to be detected at an early stage, maintenance measures to be planned in a targeted manner and downtime to be avoided. This not only ensures your productivity and ability to deliver, but also sustainably increases the efficiency and service life of your systems.
What advantages does predictive maintenance with Delphin offer?
- Reliably avoid failures: Early identification of deviations and wear protects against expensive downtimes.
- Plannable, targeted maintenance: Maintenance can be planned and does not cause any interruptions to ongoing operations.
- Maximum system availability: High productivity thanks to reduced downtimes and stable processes.
- Ensure product quality: Stable processes ensure the required product quality.
- Fast and precise diagnosis: Through combined analysis of vibration and process data.
- High-resolution real-time data: Detailed analyses through online evaluation of large amounts of data.
- Integration into existing systems: Easy connection to control technology via PROFIBUS, Modbus TCP or OPC UA.
- Also for existing systems: Retrofittable for older system generations, independent of interface diversity.
- Expandable for predictive maintenance: Gradual expansion from existing condition monitoring to predictive maintenance.
- Cost reduction: Reduced maintenance and downtime costs thanks to plannable measures and fewer unplanned repairs.
- Secure a competitive advantage: Sustainable increase in efficiency and system service life.
Use predictive maintenance to detect faults at an early stage, increase system availability and reduce maintenance costs. Typical areas of application are
- Vibration monitoring on gearboxes, bearings and shafts
- Shaft vibration analysis for plain bearing shafts
- Long-term monitoring of vibration and process data
- Frequency analysis of large amounts of data (FFT)
- Combined recording of vibration and process data
- Integration into existing process control technology
- Retrofitting existing systems
- Analysis of complex relationships in data sets
- Step-by-step expansion to predictive maintenance
Practical example of predictive maintenance
Predictive maintenance for trouble-free production
A leading manufacturer of generators approached us with the aim of avoiding unplanned downtime and making maintenance plannable in order to reduce costs and sustainably increase system availability. Together, we analyzed existing condition monitoring solutions and expanded them specifically for predictive maintenance. In the process, vibration and process data were continuously compared with our Expert Vibro devices and the ProfiSignal software, analyzed and evaluated in real time. By monitoring vibrations on gearboxes, bearings and shafts, imbalances and bearing damage could be detected and rectified at an early stage before failures occurred. Frequency analyses made it possible to identify damage patterns so that maintenance measures could be carried out in a targeted manner and at the optimum time. The data analysis was seamlessly integrated into the customer's existing control technology via OPC UAThis meant that the existing processes were not disrupted. The customer's older generators could also be easily retrofitted so that predictive maintenance could be implemented across the entire machine park.
The result: the customer was able to reduce unplanned downtime by more than 40 %, noticeably reduce maintenance costs and significantly increase the availability of its generators. Predictive maintenance has enabled the transition from reactive to predictive maintenance, making a decisive contribution to increasing efficiency and ensuring product quality.