plant failed again?
Take intelligent prevention now!
Do you want to avoid plant failures and machine downtimes at all costs? Do you want to perform intelligent and continuous condition monitoring and predictive maintenance of your machines and plants? Do you want to implement efficient and proactive maintenance of your machinery and equipment using artificial intelligence instead of relying on traditional maintenance systems? Then you are obviously in need of a smart predictive maintenance solution that meets the requirements of Industry 4.0. Wondering what you need for this? That’s easy: your collected data and our software solution s.maintenance.
our software solution s.maintenance
Thanks to our data-driven software s.maintenance, we can not only make precise predictions about the remaining service life of wear components, but also maximise it and initiate maintenance activities early. Thus, s.maintenance helps achieve a more efficient use of resources and cut maintenance costs. Our predictive maintenance software takes into account the actual wear and predicts component wear before any machine failure can occur. This allows you to keep a permanent eye on the current functional state of your plant or machine, contributes to a more effective spare parts management and significantly reduces and optimises your service/maintenance intervals and activities.
the algorithms of s.maintenance
The wear and tear of parts mainly depends on factors such as process parameters and intensity of use. Applying a machine learning approach, the algorithm of s.maintenance uses this information to create a precise model describing the wear-out dynamics of a specific component. On this basis, it can make precise predictions as to when critical usage or failure limits are reached. The result is a continuous, intelligent and automatic monitoring system for relevant wear components that is used to plan and create a predictive maintenance schedule for the machine or an entire production line.
historical time series data of sensors
wear components replacement times
other maintenance data from historical maintenance schedules
OUR SOLUTION FOR
- identify patterns and correlations in the behaviour of relevant wear components, such as filters, fans, spray valves, etc.
- understand individual wear-out dynamics and create a predictive maintenance schedule or maintenance strategy for entire production lines
- precise prediction as to when undesired operating states such as usage or failure limits of monitored wear parts are reached to make best use of service life
- predictive maintenance of your machinery and plants
- avoidance of downtime
- efficient use of resources
- effective reduction of maintenance costs
- user-defined programming interface (API) for planned derivations