Our Retrofit & Predict package for rotating equipment gives you full control over the health status of your machines — simply, quickly and reliably. Together with our partner KSB, we offer you a unique solution — from sensor installation to continuous data monitoring.
When should you choose the Retrofit & Predict package?
You don't have any sensors yet and would like to monitor the vibrations and temperatures of your machine
You want to detect and fix machine faults at an early stage
You want to avoid failures & save maintenance costs
Your benefits at a glance
Clear dashboard for all KPIs of the temperature and vibration analysis
Fast integration and installation: all services from a single source
Early detection of faults to avoid expensive repair costs
Maximize machine availability & lifetime
Retrofit & Predict
1.000€
Nonrecurring setup fee: €2.500*
Minimum duration: 24 months
Optional: Installation of hardware by aiomatic: € 2.500
* Data connections outside our standard setup are connected individually at a daily rate of 960€. **Price is for 10 use cases (minimum number)
Which maintenance solution is the right choice for you?
Find out in just 2 minutes how well your company is already positioned - and which solution perfectly fits your requirements. Answer the questions now and take your maintenance to the next level.
Conveyor belts depend on drive, transmission and clutch.
Failure makes the entire conveyor belt unusable.
Repairing needs to be done quickly to minimize downtime.
Currently often used until total failure.
Goal: smart maintenance.
Automatic monitoring through our software
First step: retrofitting sensors to conveyor belts with a length of more than 800 meters
AI learns various operating states & detects deviations
Major fault was detected at an early stage & failure could be prevented
Improved overview of extensive systems — fewer manual inspections required.
Maintenance in the energy sector
Potential of AI monitoring
Great, that worked!
Ensuring energy supply and efficiency
Requirements on energy suppliers are increasing — traditional maintenance is reaching its limits. In our white paper, we explain how AI optimizes processes and redefines machine monitoring in the energy sector.
Why traditional machine monitoring is reaching its limits
How AI monitoring effectively protects complex components from failures
What to consider when getting started with AI-based monitoring
Ready for the future of energy supply? Discover efficiency potential now.
Sensors are installed on your rotating machines — quickly and easily.
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Your data is securely transferred and visualized on the dashboard.
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Temperature and vibration analysis: Our AI models detect errors at an early stage and send warning messages.
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Continuous optimization of models for even more precise monitoring.
"The set-up with aiomatic was surprisingly straightforward. The seamless integration into existing workflows and the intuitive operation of the dashboard make the solution a real success that quickly delivers added value for our team."
Ben Thurnwald
Managing Director
"The future of maintenance is difficult when it comes to skilled labor. With the AI-based solution from aiomatic, however, this looks much brighter. The team can now focus on other activities, while monitoring and early warning alerts are provided by aiomatic."
Andreas Weber
Technical maintenance manager
“aiomatic is pioneering. For example, the application of AI to real-time data has already predicted imminent warehouse damage for one of our systems. As a result, we were able to act early and avoid an unplanned downtime of more than 8 hours. ”
Dirk Schlamann
Lead Maintenance
"Thanks to the early warning from aiomatic, we were able to detect a gearbox failure in time. As a result, we were able to maintain operations over the weekend with reduced output and prepare for the necessary repair, instead of being surprised by a sudden imminent failure."
Ben Thurnwald
Managing Director
“The aiomatic solution offers a fast and reliable way to achieve a zero-defect goal in the area of predictive maintenance compared to traditional methods.”
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